1.
Color depth
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For consumer video standards, such as High Efficiency Video Coding, the bit depth specifies the number of bits used for each color component. When referring to a pixel the concept can be defined as bits per pixel, which specifies the number of bits used. When referring to a component the concept can be defined as bits per component, bits per channel, bits per color. Color depth is one aspect of color representation, expressing how finely levels of color can be expressed. The definition of both color precision and gamut is accomplished with a color encoding specification which assigns a code value to a location in a color space. Comparison, same image on five different color depths, different looks, but also different file sizes. With the relatively low color depth, the value is typically a number representing the index into a color map or palette. The colors available in the palette itself may be fixed by the hardware or modifiable within the limits of the hardware, modifiable palettes are sometimes referred to as pseudocolor palettes. 1-bit color, monochrome, often black and white, compact Macintoshes, 2-bit color, CGA, gray-scale early NeXTstation, color Macintoshes, Atari ST. 12-bit color, some Silicon Graphics systems, Color NeXTstation systems, for example, in the ZX Spectrum, the picture is stored in a two-color format, but these two colors can be separately defined for each rectangular block of 8x8 pixels. A very limited but true direct color system, there are 3 bits for each of the R and G components, the normal human eye is less sensitive to the blue component than to the red or green, so it is assigned one bit less than the others. Used, amongst others, in the MSX2 system series of computers in the early to mid 1990s, do not confuse with an indexed color depth of 8bpp. High color supports 15/16-bit for three RGB colors, in 16-bit direct color, there can be 4 bits for each of the R, G, and B components, plus optionally 4 bits for alpha, enabling 4,096 different colors with 16 levels of transparency. Or in some systems there can be 5 bits per color component and 1 bit of alpha, or there can be 5 bits for red,6 bits for green and these color depths are sometimes used in small devices with a color display, such as mobile telephones. Variants with 5 or more bits per component are sometimes called high color. More expensive LCDs can display 24-bit or greater color depth, true color supports 24-bit for three RGB colors. Usually, true color is defined to mean 256 shades of red, green, the human eye can discriminate up to ten million colors. Color processing in the eye occurs through retinal cone cells which are of three types, although not corresponding to red, green and blue hues, true color can also refer to an RGB display-mode that does not need a color look-up table
2.
Grayscale
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In photography and computing, a grayscale or greyscale digital image is an image in which the value of each pixel is a single sample, that is, it carries only intensity information. Images of this sort, also known as black-and-white, are composed exclusively of shades of gray, grayscale images are distinct from one-bit bi-tonal black-and-white images, which in the context of computer imaging are images with only two colors, black and white. Grayscale images have many shades of gray in between, but also they can be synthesized from a full color image, see the section about converting to grayscale. The intensity of a pixel is expressed within a range between a minimum and a maximum, inclusive. This range is represented in a way as a range from 0 and 1. This notation is used in papers, but this does not define what black or white is in terms of colorimetry. Another convention is to employ percentages, so the scale is then from 0% to 100%. This is used for an intuitive approach, but if only integer values are used, the range encompasses a total of only 101 intensities. Also, the notation is used in printing to denote how much ink is employed in halftoning. In computing, although the grayscale can be computed through rational numbers, image pixels are stored in binary, the precision provided by this format is barely sufficient to avoid visible banding artifacts, but very convenient for programming because a single pixel then occupies a single byte. Technical uses often require more levels, to full use of the sensor accuracy. Sixteen bits per sample is a convenient choice for such uses, the TIFF and the PNG image file formats support 16-bit grayscale natively, although browsers and many imaging programs tend to ignore the low order 8 bits of each pixel. No matter what pixel depth is used, the binary representations assume that 0 is black, a common strategy is to use the principles of photometry or, more broadly, colorimetry to match the luminance of the grayscale image to the luminance of the original color image. This also ensures that both images will have the absolute luminance, as can be measured in its SI units of candelas per square meter, in any given area of the image. Then, linear luminance is calculated as a sum of the three linear-intensity values. To encode grayscale intensity in linear RGB, each of the three primaries can be set to equal the calculated linear luminance Y, linear luminance typically needs to be gamma compressed to get back to a conventional non-linear representation. In practice, because the three components are then equal, it is only necessary to store these values once in sRGB-compatible image formats that support a single-channel representation. The ITU-R BT.709 standard used for HDTV developed by the ATSC uses different color coefficients, although these are numerically the same coefficients used in sRGB above, the effect is different because here they are being applied directly to gamma-compressed values
3.
8-bit color
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8-bit color graphics is a method of storing image information in a computers memory or in an image file, such that each pixel is represented by one 8-bit byte. The maximum number of colors that can be displayed at any one time is 256, there are two forms of 8-bit color graphics. The most common uses a palette of 256 colors, where each of the 256 entries in the palette map is given red, green. In most color maps, each color is chosen from a palette of 16,777,216 colors. But in the original VGA cards 320x200 mode,256 on-screen colors could be chosen from a palette of 262,144 colors, some older cards prior to the VGA can only choose the 256-color palette from 4,096 colors. The other form is where the 8 bits directly describe red, green and this second form is often called 8-bit truecolor, as it does not use a palette at all, and is thus more similar to the 15-bit, 16-bit, and 24-bit truecolor modes. Bit 76543210 Data R R R G G G B B Most 8-bit image formats store a local palette of 256 colors in addition to the raw image data. If such an image is to be displayed on 8-bit graphics hardware and this can result in other images on the screen having wildly distorted colors due to differences in their palettes. For this reason, on 8-bit graphics hardware, programs such as web browsers must address this issue when simultaneously displaying multiple images from different sources. Each image may have its own palette, but the colors in each image will be remapped to a single palette, currently, most graphics hardware runs in 24-bit truecolor or 32-bit truecolor, and this problem is largely a thing of the past. However, some remote desktop software can switch to 8-bit color to conserve bandwidth, planar Packed pixel 15/16-bit color 24-bit color Color depth List of palettes
4.
High color
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High color graphics is a method of storing image information in a computers memory such that each pixel is represented by two bytes. Usually the color is represented by all 16 bits, but some also support 15-bit high color. More recently, high color has been used by Microsoft to distinguish display systems that can make use of more than 8-bits per color channel from traditional 8-bit per color channel formats and this is a distinct usage from the 15-bit or 16-bit formats traditionally associated with the phrase high color. This allows 32,768 possible colors for each pixel, when all 16 bits are used, one of the components gets an extra bit, allowing 64 levels of intensity for that component, and a total of 65,536 available colors. This can lead to discrepancies in encoding, e. g. when one wishes to encode the 24-bit color RGB with 16 bits. The red and blue channels will take the five most significant bits, the green channel, with six bits of precision, will have a binary value of 001010, or 10 on a scale from 0 to 63. Because of this, the color RGB will have a purple tinge when displayed in 16 bits. Note that 40 on a scale from 0 to 255 is 15. 7%, green is usually chosen for the extra bit in 16 bits because the human eye has its highest sensitivity for green shades. For a demonstration, look closely at the picture where dark shades of red, green. Readers with normal vision should see the individual shades of green relatively easily, while the shades of red should be difficult to see, and the shades of blue are likely indistinguishable. More rarely, some systems support having the extra bit of depth on the red or blue channel. As a result, some image formats can save paletted 16-bit images with an embedded CLUT, 24-bit color 30/36/48-bit color Color depth Planar Packed pixel List of monochrome and RGB palettes — 15-bit RGB and 16-bit RGB sections
5.
Indexed color
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It is a form of vector quantization compression. When an image is encoded in this way, color information is not directly carried by the pixel data, but is stored in a separate piece of data called a palette. Every element in the array represents a color, indexed by its position within the array, the individual entries are sometimes known as color registers. The image pixels do not contain the full specification of its color and this technique is sometimes referred as pseudocolor or indirect color, as colors are addressed indirectly. Perhaps the first device that supported palette colors was a frame buffer, described in 1975 by Kajiya, Sutherland. This supported a palette of 256 36-bit RGB colors, the palette itself stores a limited number of distinct colors,4,16 or 256 are the most common cases. These limits are imposed by the target architectures display adapter hardware. While 256 values can be fit into a single 8-bit byte, sometimes, 1-bit values can be used, and then up to eight pixels can be packed into a single byte, such images are considered binary images and not an indexed color image. If simple video overlay is intended through a transparent color, one entry is specifically reserved for this purpose. Some machines, such as the MSX series, had the transparent color reserved by hardware, indexed color images with palette sizes beyond 256 entries are rare. The practical limit is around 12-bit per pixel,4,096 different indices, to use indexed 16 bpp or more does not provide the benefits of the indexed color images nature, due to the color palette size in bytes being greater than the raw image data itself. Also, useful direct RGB Highcolor modes can be used from 15 bpp, if an image has many subtle color shades, it is necessary to select a limited repertoire of colors to approximate the image using color quantization. Such a palette is frequently insufficient to represent the image accurately, many early personal and home computers had very limited hardware palettes that could produce a very small set of colors. In these cases, each pixels value mapped directly onto one of these colors, well-known examples include the Apple II, Commodore 64 and IBM PC CGA, all of which included hardware that could produce a fixed set of 16 colors. In these cases, an image can encode each pixel with 4-bits, in most cases, however, the display hardware supports additional modes where only a subset of those colors can be used in a single image, a useful technique to save memory. For instance, the CGAs 320×200 resolution mode could show only four of the 16 colors at one time, as the palettes were entirely proprietary, an image generated on one platform cannot be directly viewed properly on another. Other machines of this era had the ability to generate a set of colors. Examples include the 256-color palette on Atari 8-bit machines or the 4,096 colors of the VT241 terminal in ReGIS graphics mode
6.
Palette (computing)
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In computer graphics, a palette is a finite set of colors. Palettes can be optimized to improve accuracy in the presence of software or hardware constraints. Depending on the context, the palette and related terms such as Web palette. Selected colors or picked colors, In this case, the selection, generally from a wider explicitly available full palette, is always chosen by software. For example, the standard VGA display adapter is said to provide a palette of 256 simultaneous colors from a total of 262,144 different colors, default palette or system palette, The given selected colors have been officially standardized by some body or corporation. For example, the well known Web-safe colors for use with Internet browsers, on an individual image, color map or color table, The limited color selection is stored inside the given indexed color image file. For example, the registers of the Commodore Amiga are known both as their color palette and their CLUT, depending on sources. GUI palettes An arrangement of a set of user or system colors that can be chosen. In such cases, the color palette or user color palette are common equivalents. This usage resembles a true artists palette, a palette for choosing colors can be also a floating palette. An application can, in turn, show different image thumbnails in a mosaic on screen. It is obvious that the program cannot load all the adaptive palettes of every displayed image thumbnail at the time in the hardware color registers. A solution is to use a unique, common master palette or universal palette and this is done by selecting colors in such way that the master palette comprises a full RGB color space in miniature, limiting the possible levels that the red, green and blue components may have. This kind of arrangement is referred as a uniform palette. The normal human eye has sensibility to the three colors in different degrees, the more to the green, the less to the blue. So RGB arrangements can take advantage of this by assigning more levels for the green component and it is more general to use only 6R×6G×6B =216, 6R×8G×5B =240 or 6R×7G×6B =252, which leave room for some reserved colors. This way, and with further dithering, the color image can nearly match the original. But this creates a heavy dependence between the pixels and its adaptive palette
7.
RGB color model
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The RGB color model is an additive color model in which red, green and blue light are added together in various ways to reproduce a broad array of colors. The name of the model comes from the initials of the three primary colors, red, green and blue. Before the electronic age, the RGB color model already had a theory behind it. Thus an RGB value does not define the same color across devices without some kind of color management, typical RGB input devices are color TV and video cameras, image scanners, video games, and digital cameras. Typical RGB output devices are TV sets of technologies, computer and mobile phone displays, video projectors, multicolor LED displays. Color printers, on the hand are not RGB devices. This article discusses concepts common to all the different color spaces that use the RGB color model, to form a color with RGB, three light beams must be superimposed. Each of the three beams is called a component of color, and each of them can have an arbitrary intensity, from fully off to fully on. The RGB color model is additive in the sense that the three beams are added together, and their light spectra add, wavelength for wavelength. This is essentially opposite to the color model that applies to paints, inks, dyes. When the intensities for all the components are the same, the result is a shade of gray, darker or lighter depending on the intensity. When the intensities are different, the result is a colorized hue, a secondary color is formed by the sum of two primary colors of equal intensity, cyan is green+blue, magenta is red+blue, and yellow is red+green. The RGB color model itself does not define what is meant by red, green and blue colorimetrically, and so the results of mixing them are not specified as absolute, but relative to the primary colors. When the exact chromaticities of the red, green and blue primaries are defined, the normal three kinds of light-sensitive photoreceptor cells in the human eye respond most to yellow, green, and violet light. As an example, suppose that light in the range of wavelengths enters the eye. Light of these wavelengths would activate both the medium and long wavelength cones of the retina, but not equally—the long-wavelength cells will respond more, the difference in the response can be detected by the brain, and this difference is the basis of our perception of orange. Thus, the appearance of an object results from light from the object entering our eye and stimulating the different cones simultaneously. The first experiments with RGB in early color photography were made in 1861 by Maxwell himself, to reproduce the color photograph, three matching projections over a screen in a dark room were necessary
8.
Pixel
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The address of a pixel corresponds to its physical coordinates. LCD pixels are manufactured in a grid, and are often represented using dots or squares. Each pixel is a sample of an image, more samples typically provide more accurate representations of the original. The intensity of each pixel is variable, in color imaging systems, a color is typically represented by three or four component intensities such as red, green, and blue, or cyan, magenta, yellow, and black. The word pixel is based on a contraction of pix and el, the word pixel was first published in 1965 by Frederic C. Billingsley of JPL, to describe the elements of video images from space probes to the Moon. Billingsley had learned the word from Keith E. McFarland, at the Link Division of General Precision in Palo Alto, McFarland said simply it was in use at the time. The word is a combination of pix, for picture, the word pix appeared in Variety magazine headlines in 1932, as an abbreviation for the word pictures, in reference to movies. By 1938, pix was being used in reference to pictures by photojournalists. The concept of a picture element dates to the earliest days of television, some authors explain pixel as picture cell, as early as 1972. In graphics and in image and video processing, pel is often used instead of pixel, for example, IBM used it in their Technical Reference for the original PC. A pixel is generally thought of as the smallest single component of a digital image, however, the definition is highly context-sensitive. For example, there can be printed pixels in a page, or pixels carried by electronic signals, or represented by digital values, or pixels on a display device, or pixels in a digital camera. This list is not exhaustive and, depending on context, synonyms include pel, sample, byte, bit, dot, Pixels can be used as a unit of measure such as,2400 pixels per inch,640 pixels per line, or spaced 10 pixels apart. For example, a high-quality photographic image may be printed with 600 ppi on a 1200 dpi inkjet printer, even higher dpi numbers, such as the 4800 dpi quoted by printer manufacturers since 2002, do not mean much in terms of achievable resolution. The more pixels used to represent an image, the closer the result can resemble the original, the number of pixels in an image is sometimes called the resolution, though resolution has a more specific definition.3 megapixels. The pixels, or color samples, that form an image may or may not be in one-to-one correspondence with screen pixels. In computing, a composed of pixels is known as a bitmapped image or a raster image
9.
Monochrome
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Monochrome describes paintings, drawings, design, or photographs in one color or values of one color. A monochromatic object or image reflects colors in shades of limited colors or hues, images using only shades of grey are called grayscale or black-and-white. However, scientifically speaking, monochromatic light refers to light of a narrow band of wavelengths. In computing, monochrome has two meanings, it may mean having only one color which is either on or off, a monochrome computer display is able to display only a single color, often green, amber, red or white, and often also shades of that color. In film photography, monochrome is typically the use of black-and-white film, originally, all photography was done in monochrome. Although color photography was possible even in the late 19th century, easily used color films, if the red channel is eliminated and the green and blue combined then the effect will be similar to that of orthochromatic film or the use of a cyan filter on panchromatic film. The selection of weighting thus allows a range of artistic expression in the final monochromatic image. For production of an image the original color stereogram source may first be reduced to monochrome in order to simplify the rendering of the image. This is sometimes required in cases where an image would render in a confusing manner given the colors and patterns present in the source image. In physics, monochromatic light is electromagnetic radiation of a single frequency, even very controlled sources such as lasers operate in a range of frequencies. In practice, filtered light, diffraction grating separated light and laser light are all referred to as monochromatic. Often light sources can be compared and one be labeled as “more monochromatic”
10.
Adobe Photoshop
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Adobe Photoshop is a raster graphics editor developed and published by Adobe Systems for macOS and Windows. Photoshop was created in 1988 by Thomas and John Knoll and it can edit and compose raster images in multiple layers and supports masks, alpha compositing and several color models including RGB, CMYK, CIELAB, spot color and duotone. Photoshop has vast support for file formats but also uses its own PSD. In addition to graphics, it has limited abilities to edit or render text, vector graphics, 3D graphics. Photoshops featureset can be expanded by Photoshop plug-ins, programs developed and distributed independently of Photoshop that can run inside it, Photoshops naming scheme was initially based on version numbers. Photoshop CS3 through CS6 were also distributed in two different editions, Standard and Extended, in June 2013, with the introduction of Creative Cloud branding, Photoshops licensing scheme was changed to that of software as a service rental model and the CS suffixes were replaced with CC. Historically, Photoshop was bundled with software such as Adobe ImageReady, Adobe Fireworks, Adobe Bridge, Adobe Device Central. Alongside Photoshop, Adobe also develops and publishes Photoshop Elements, Photoshop Lightroom, Photoshop Express, collectively, they are branded as The Adobe Photoshop Family. It is currently a licensed software, Photoshop was developed in 1987 by the American brothers Thomas and John Knoll, who sold the distribution license to Adobe Systems Incorporated in 1988. Thomas Knoll, a PhD student at the University of Michigan, began writing a program on his Macintosh Plus to display images on a monochrome display. This program, called Display, caught the attention of his brother John Knoll, an Industrial Light & Magic employee, Thomas took a six-month break from his studies in 1988 to collaborate with his brother on the program. Thomas renamed the program ImagePro, but the name was already taken, during this time, John traveled to Silicon Valley and gave a demonstration of the program to engineers at Apple and Russell Brown, art director at Adobe. Both showings were successful, and Adobe decided to purchase the license to distribute in September 1988, while John worked on plug-ins in California, Thomas remained in Ann Arbor writing code. Photoshop 1.0 was released on 19 February 1990 for Macintosh exclusively, the Barneyscan version included advanced color editing features that were stripped from the first Adobe shipped version. The handling of color slowly improved with each release from Adobe, at the time Photoshop 1.0 was released, digital retouching on dedicated high end systems, such as the Scitex, cost around $300 an hour for basic photo retouching. Photoshop files have default file extension as. PSD, which stands for Photoshop Document, a PSD file stores an image with support for most imaging options available in Photoshop. These include layers with masks, transparency, text, alpha channels and spot colors, clipping paths and this is in contrast to many other file formats that restrict content to provide streamlined, predictable functionality. A PSD file has a height and width of 30,000 pixels
11.
Digital image processing
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Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of signal processing, digital image processing has many advantages over analog image processing. It allows a wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise. Since images are defined over two dimensions digital image processing may be modeled in the form of multidimensional systems, the cost of processing was fairly high, however, with the computing equipment of that era. That changed in the 1970s, when digital image processing proliferated as cheaper computers, images then could be processed in real time, for some dedicated problems such as television standards conversion. As general-purpose computers became faster, they started to take over the role of dedicated hardware for all but the most specialized, Digital image processing technology for medical applications was inducted into the Space Foundation Space Technology Hall of Fame in 1994. In 2002 Raanan Fattel, introduced Gradient domain image processing, a new way to process images in which the differences between pixels are manipulated rather than the values themselves. J. Fundamentals of Digital Image Processing, A Practical Approach with Examples in Matlab, Digital Image Processing, An Algorithmic Approach Using Java. R. Fisher, K Dawson-Howe, A. Fitzgibbon, C, dictionary of Computer Vision and Image Processing. Gonzalez, Richard E. Woods, Steven L. Eddins, milan Sonka, Vaclav Hlavac, Roger Boyle. Image Processing, Analysis, and Machine Vision, lectures on Image Processing, by Alan Peters. IPRG Open group related to image processing research resources Processing digital images with computer algorithms IPOL Open research journal on image processing with software and web demos
12.
Mask (computing)
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In computer science, a mask is data that is used for bitwise operations, particularly in a bit field. Using a mask, multiple bits in a byte, nibble, word can be set either on, to turn certain bits on, the bitwise OR operation can be used, following the principle that Y OR1 =1 and Y OR0 = Y. Therefore, to make sure a bit is on, OR can be used with a 1, to leave a bit unchanged, OR is used with a 0. Example, Masking on the higher nibble the lower nibble unchanged,1001010110100101 OR1111000011110000 =1111010111110101 More often, in practice, bits that are to be ignored are masked off rather than masked on. There is no way to change a bit from on to off using the OR operation, when a value is ANDed with a 1, the result is simply the original value, as in, Y AND1 = Y. However, ANDing a value with 0 is guaranteed to return a 0, to leave the other bits alone, use AND with 1. Example, Masking off the higher nibble the lower nibble unchanged,1001010110100101 AND0000111100001111 =0000010100000101 It is possible to use bitmasks to easily check the state of individual bits regardless of the other bits. To do this, turning off all the other using the bitwise AND is done as discussed above. If it is equal to 0, then the bit was off, what makes this convenient is that it is not necessary to figure out what the value actually is, just that it is not 0. Example, Querying the status of the 4th bit 1001110110010101 AND0000100000001000 =0000100000000000 So far the article has covered how to turn bits on and turn bits off, but not both at once. Sometimes it does not really matter what the value is, and this can be achieved using the XOR operation. XOR returns 1 if and only if an odd number of bits are 1, therefore, if two corresponding bits are 1, the result will be a 0, but if only one of them is 1, the result will be 1. Therefore inversion of the values of bits is done by XORing them with a 1, if the original bit was 1, it returns 1 XOR1 =0. If the original bit was 0 it returns 0 XOR1 =1, also note that XOR masking is bit-safe, meaning that it will not affect unmasked bits because Y XOR0 = Y, just like an OR. For example, in the graphics API OpenGL, there is a command and it can clear up to four buffers, so the API authors could have had it take four arguments. But then a call to it would look like which is not very descriptive, for example, an implementation of glClear might look like, The advantage to this approach is that function argument overhead is decreased. Since the minimum size is one byte, separating the options into separate arguments would be wasting seven bits per argument. Instead, functions typically accept one or more 32-bit integers, with up to 32 option bits in each, while elegant, in the simplest implementation this solution is not type-safe
13.
Image segmentation
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In computer vision, image segmentation is the process of partitioning a digital image into multiple segments. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful, Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in a such that pixels with the same label share certain characteristics. The result of image segmentation is a set of segments that cover the entire image. Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, adjacent regions are significantly different with respect to the same characteristic. To be useful, these techniques must typically be combined with a specific knowledge in order to effectively solve the domains segmentation problems. The simplest method of image segmentation is called the thresholding method and this method is based on a clip-level to turn a gray-scale image into a binary image. There is also a balanced histogram thresholding, the key of this method is to select the threshold value. Several popular methods are used in industry including the maximum entropy method, Otsus method, recently, methods have been developed for thresholding computed tomography images. The key idea is that, unlike Otsus method, the thresholds are derived from the radiographs instead of the image, new methods suggested the usage of multi-dimensional fuzzy rule-based non-linear thresholds. The K-means algorithm is a technique that is used to partition an image into K clusters. The difference is based on pixel color, intensity, texture. K can be selected manually, randomly, or by a heuristic and this algorithm is guaranteed to converge, but it may not return the optimal solution. The quality of the solution depends on the set of clusters. Compression based methods postulate that the segmentation is the one that minimizes, over all possible segmentations. The connection between two concepts is that segmentation tries to find patterns in an image and any regularity in the image can be used to compress it. The method describes each segment by its texture and boundary shape and this prior is used by Huffman coding to encode the difference chain code of the contours in an image. Thus, the smoother a boundary is, the coding length it attains
14.
Thresholding (image processing)
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Thresholding is the simplest method of image segmentation. From a grayscale image, thresholding can be used to create binary images, in the example image on the right, this results in the dark tree becoming completely black, and the white snow becoming completely white. To make thresholding completely automated, it is necessary for the computer to select the threshold T. Object Attribute-based methods search a measure of similarity between the gray-level and the images, such as fuzzy shape similarity, edge coincidence. Spatial methods use higher-order probability distribution and/or correlation between pixels Local methods adapt the threshold value on each pixel to the image characteristics. In these methods, a different T is selected for each pixel in the image, colour images can also be thresholded. One approach is to designate a separate threshold for each of the RGB components of the image and this reflects the way the camera works and how the data is stored in the computer, but it does not correspond to the way that people recognize colour. Therefore, the HSL and HSV colour models are often used. It is also possible to use the CMYK colour model, histogram shape-based methods in particular, but also many other thresholding algorithms, make certain assumptions about the image intensity probability distribution. The most common thresholding methods work on distributions, but algorithms have also been developed for unimodal distributions, multimodal distributions. Otsus Method Balanced histogram thresholding Pham N, Morrison A, Schwock J et al, quantitative image analysis of immunohistochemical stains using a CMYK color model. Shapiro, Linda G. & Stockman, George C, ISBN 0-13-030796-3 Mehmet Sezgin and Bulent Sankur, Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging 13, 146–165. In Digital Image Processing, pp. 595–611, ISBN 81-7808-629-8 M. Luessi, M. Eichmann, G. M. Schuster, and A. K. Katsaggelos, Framework for efficient optimal multilevel image thresholding, Journal of Electronic Imaging, vol. Lai, P. L. Rosin, Efficient Circular Thresholding, IEEE Trans. on Image Processing 23, pp. 992–1001
15.
Dither
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Dither is an intentionally applied form of noise used to randomize quantization error, preventing large-scale patterns such as color banding in images. Dither is routinely used in processing of digital audio and video data, and is often one of the last stages of mastering audio to a CD. A typical use of dither is converting an image to black and white. …ne of the earliest of dither came in World War II, airplane bombers used mechanical computers to perform navigation and bomb trajectory calculations. Curiously, these computers performed more accurately when flying on board the aircraft, engineers realized that the vibration from the aircraft reduced the error from sticky moving parts. Instead of moving in short jerks, they moved more continuously, small vibrating motors were built into the computers, and their vibration was called dither from the Middle English verb didderen, meaning to tremble. Today, when you tap a mechanical meter to increase its accuracy, you are applying dither, in minute quantities, dither successfully makes a digitization system a little more analog in the good sense of the word. The term dither was published in books on analog computation and hydraulically controlled guns shortly after the Second World War. The concept of dithering to reduce quantization patterns was first applied by Lawrence G. Roberts in his 1961 MIT masters thesis and 1962 article though he did not use the term dither, by 1964 dither was being used in the modern sense described in this article. Dither is often used in audio and video processing. It is utilized in different fields where digital processing and analysis are used. These uses include systems using digital processing, such as digital audio, digital video, digital photography, seismology, radar, weather forecasting systems. The premise is that quantization yields error, if that error is repeating and correlated to the signal, the error that results is repeating, cyclical, and mathematically determinable. In some fields, especially where the receptor is sensitive to such artifacts, in these fields introducing dither results in less determinable artifacts. The field of audio is an example of this. The human ear functions much like a Fourier transform, wherein it hears individual frequencies, in audio, dither can be useful to break up periodic limit cycles, which are a common problem in digital filters. Random noise is less objectionable than the harmonic tones produced by limit cycles. Gaussian noise requires a level of added noise for full elimination of distortion than do rectangular PDF or triangular PDF noise
16.
Laser printing
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Laser printing is an electrostatic digital printing process. It produces high-quality text and graphics by repeatedly passing a laser beam back, the drum then selectively collects electrically charged powdered ink, and transfers the image to paper, which is then heated in order to permanently fuse the text and/or imagery. As with digital photocopiers, laser printers employ a xerographic printing process, however, laser printing differs from analog photocopiers in that the image is produced by the direct scanning of the medium across the printers photoreceptor. This enables laser printing to copy images more quickly than most photocopiers, invented at Xerox PARC in the 1970s, laser printers were introduced for the office and then home markets in subsequent years by IBM, Canon, Xerox, Apple, Hewlett-Packard and many others. Over the decades, quality and speed have increased as price has fallen, in the 1960s, the Xerox Corporation held a dominant position in the photocopier market. In 1969, Gary Starkweather, who worked in Xeroxs product development department, had the idea of using a beam to draw an image of what was to be copied directly onto the copier drum. After transferring to the recently formed Palo Alto Research Center in 1971, in 1972, Starkweather worked with Butler Lampson and Ronald Rider to add a control system and character generator, resulting in a printer called EARS —which later became the Xerox 9700 laser printer. The first commercial implementation of a printer was the IBM3800 in 1976. It was designed for data centers, where it replaced line printers attached to mainframe computers, the IBM3800 was used for high-volume printing on continuous stationery, and achieved speeds of 215 pages per minute, at a resolution of 240 dots per inch. Over 8,000 of these printers were sold, the Xerox 9700 was brought to market in 1977. Unlike the IBM3800, the Xerox 9700 was not targeted to any particular existing printers. The Xerox 9700 excelled at printing high-value documents on paper with varying content. In 1979, inspired by the Xerox 9700s commercial success, Japanese camera and optics company, Canon, developed a low-cost, desktop laser printer, Canon then began work on a much-improved print engine, the Canon CX, resulting in the LBP-CX printer. Lacking experience in selling to computer users, Canon sought partnerships with three Silicon Valley companies, Diablo Data Systems, Hewlett-Packard, and Apple Computer, the first laser printer designed for office use reached market in 1981, the Xerox Star 8010. The system used a metaphor that was unsurpassed in commercial sales. Although it was innovative, the Star workstation was an expensive system, affordable only to a fraction of the businesses. The first laser printer intended for mass-market sales was the HP LaserJet, released in 1984, it used the Canon CX engine, the LaserJet was quickly followed by printers from Brother Industries, IBM, and others. First-generation machines had large photosensitive drums, of greater than the loaded papers length
17.
Fax
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Fax, sometimes called telecopying or telefax, is the telephonic transmission of scanned printed material, normally to a telephone number connected to a printer or other output device. The receiving fax machine interprets the tones and reconstructs the image, early systems used direct conversions of image darkness to audio tone in a continuous or analog manner. Since the 1980s, most machines modulate the audio frequencies using a digital representation of the page which is compressed to quickly transmit areas which are all-white or all-black. Scottish inventor Alexander Bain worked on chemical mechanical fax type devices and he received British patent 9745 on May 27,1843 for his Electric Printing Telegraph. Frederick Bakewell made several improvements on Bains design and demonstrated a telefax machine, the Pantelegraph was invented by the Italian physicist Giovanni Caselli. He introduced the first commercial service between Paris and Lyon in 1865, some 11 years before the invention of the telephone. In 1880, English inventor Shelford Bidwell constructed the scanning phototelegraph that was the first telefax machine to scan any two-dimensional original, previously, photographs had been sent over the radio using this process. The Western Union Deskfax fax machine, announced in 1948, was a machine that fit comfortably on a desktop. As a designer for the Radio Corporation of America, in 1924, Richard H. Ranger invented the wireless photoradiogram, or transoceanic radio facsimile, the forerunner of today’s fax machines. A photograph of President Calvin Coolidge sent from New York to London on November 29,1924 became the first photo picture reproduced by transoceanic radio facsimile, commercial use of Ranger’s product began two years later. Also in 1924, Herbert E. Ives of AT&T Corporation transmitted and reconstructed the first color facsimile, around 1952 or so, Finch Facsimile, a highly developed machine, was described in detail in a book, it was never manufactured in quantity. By the late 1940s, radiofax receivers were sufficiently miniaturized to be fitted beneath the dashboard of Western Unions Telecar telegram delivery vehicles, in the 1960s, the United States Army transmitted the first photograph via satellite facsimile to Puerto Rico from the Deal Test Site using the Courier satellite. Radio fax is still in limited use today for transmitting weather charts, in 1964, Xerox Corporation introduced what many consider to be the first commercialized version of the modern fax machine, under the name or Long Distance Xerography. This model was superseded two years later with a unit that would set the standard for fax machines for years to come. Up until this point facsimile machines were expensive and hard to operate. In 1966, Xerox released the Magnafax Telecopiers, a smaller and this unit was far easier to operate and could be connected to any standard telephone line. This machine was capable of transmitting a letter-sized document in about six minutes, the first sub-minute, digital fax machine was developed by Dacom, which built on digital data compression technology originally developed at Lockheed for satellite communication. By the late 1970s, many companies around the world, entered the fax market, very shortly after a new wave of more compact, faster and efficient fax machines would hit the market
18.
Computer monitor
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A computer monitor or a computer display is an electronic visual display for computers. A monitor usually comprises the display device, circuitry, casing, the display device in modern monitors is typically a thin film transistor liquid crystal display or a flat panel LED display, while older monitors used a cathode ray tubes. It can be connected to the computer via VGA, DVI, HDMI, DisplayPort, Thunderbolt, LVDS or other proprietary connectors, originally, computer monitors were used for data processing while television receivers were used for entertainment. From the 1980s onwards, computers have used for both data processing and entertainment, while televisions have implemented some computer functionality. The common aspect ratio of televisions, and computer monitors, has changed from 4,3 to 16,10, to 16,9. Early electronic computers were fitted with a panel of light bulbs where the state of each particular bulb would indicate the state of a particular register bit inside the computer. This allowed the operating the computer to monitor the internal state of the machine. As early monitors were only capable of displaying a limited amount of information. Instead, a printer was the primary output device, while the monitor was limited to keeping track of the programs operation. Multiple technologies have used for computer monitors. Until the 21st century most used cathode ray tubes but they have largely superseded by LCD monitors. The first computer monitors used cathode ray tubes, high-resolution CRT displays were developed for specialized military, industrial and scientific applications but they were far too costly for general use. Either computer could be connected to the terminals of an ordinary color TV set or used with a purpose-made CRT color monitor for optimum resolution. In 1984 IBM introduced the Enhanced Graphics Adapter which was capable of producing 16 colors and had a resolution of 640 x 350, by the end of the 1980s color CRT monitors that could clearly display 1024 x 768 pixels were widely available and increasingly affordable. During the following decade maximum display resolutions gradually increased and prices continued to fall, CRT technology remained dominant in the PC monitor market into the new millennium partly because it was cheaper to produce and offered viewing angles close to 180 degrees. CRTs still offer some image quality advantages over LCDs but improvements to the latter have made much less obvious. There are multiple technologies that have used to implement liquid crystal displays. Commonly, the laptop would be offered with an assortment of display options at increasing price points, monochrome
19.
Bitmap
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In computing, a bitmap is a mapping from some domain to bits, that is, values which are zero or one. It is also called a bit array or bitmap index, in computer graphics, when the domain is a rectangle a bitmap gives a way to store a binary image, that is, an image in which each pixel is either black or white. The more general term refers to a map of pixels. Often bitmap is used for this as well, in some contexts, the term bitmap implies one bit per pixel, while pixmap is used for images with multiple bits per pixel. A bitmap is a type of organization or image file format used to store digital images. The term bitmap comes from the computer programming terminology, meaning just a map of bits, now, along with pixmap, it commonly refers to the similar concept of a spatially mapped array of pixels. Raster images in general may be referred to as bitmaps or pixmaps, whether synthetic or photographic, besides BMP, other file formats that store literal bitmaps include InterLeaved Bitmap, Portable Bitmap, X Bitmap, and Wireless Application Protocol Bitmap. Similarly, most other file formats, such as JPEG, TIFF, PNG, and GIF, also store bitmap images. In typical uncompressed bitmaps, image pixels are stored with a color depth of 1,4,8,16,24,32,48. Pixels of 8 bits and fewer can represent either grayscale or indexed color. An alpha channel may be stored in a bitmap, where it is similar to a grayscale bitmap, or in a fourth channel that, for example. The bits representing the bitmap pixels may be packed or unpacked, depending on the color depth, a pixel in the picture will occupy at least n/8 bytes, where n is the bit depth. In the formula above, header size and color palette size, due to effects of row padding to align each row start to a storage unit boundary such as a word, additional bytes may be needed. They called these device-independent bitmaps or DIBs, and the format for them is called DIB file format or BMP file format. According to Microsoft support, A device-independent bitmap is a used to define device-independent bitmaps in various color resolutions. The main purpose of DIBs is to allow bitmaps to be moved from one device to another, a DIB is an external format, in contrast to a device-dependent bitmap, which appears in the system as a bitmap object. A DIB is normally transported in metafiles, BMP files, here, device independent refers to the format, or storage arrangement, and should not be confused with device-independent color. The X Window System uses a similar XBM format for black-and-white images, numerous other uncompressed bitmap file formats are in use, though most not widely
20.
Subset
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In mathematics, especially in set theory, a set A is a subset of a set B, or equivalently B is a superset of A, if A is contained inside B, that is, all elements of A are also elements of B. The relationship of one set being a subset of another is called inclusion or sometimes containment, the subset relation defines a partial order on sets. The algebra of subsets forms a Boolean algebra in which the relation is called inclusion. For any set S, the inclusion relation ⊆ is an order on the set P of all subsets of S defined by A ≤ B ⟺ A ⊆ B. We may also partially order P by reverse set inclusion by defining A ≤ B ⟺ B ⊆ A, when quantified, A ⊆ B is represented as, ∀x. So for example, for authors, it is true of every set A that A ⊂ A. Other authors prefer to use the symbols ⊂ and ⊃ to indicate proper subset and superset, respectively and this usage makes ⊆ and ⊂ analogous to the inequality symbols ≤ and <. For example, if x ≤ y then x may or may not equal y, but if x < y, then x definitely does not equal y, and is less than y. Similarly, using the convention that ⊂ is proper subset, if A ⊆ B, then A may or may not equal B, the set A = is a proper subset of B =, thus both expressions A ⊆ B and A ⊊ B are true. The set D = is a subset of E =, thus D ⊆ E is true, any set is a subset of itself, but not a proper subset. The empty set, denoted by ∅, is also a subset of any given set X and it is also always a proper subset of any set except itself. These are two examples in both the subset and the whole set are infinite, and the subset has the same cardinality as the whole. The set of numbers is a proper subset of the set of real numbers. In this example, both sets are infinite but the set has a larger cardinality than the former set. Another example in an Euler diagram, Inclusion is the partial order in the sense that every partially ordered set is isomorphic to some collection of sets ordered by inclusion. The ordinal numbers are a simple example—if each ordinal n is identified with the set of all ordinals less than or equal to n, then a ≤ b if and only if ⊆. For the power set P of a set S, the partial order is the Cartesian product of k = |S| copies of the partial order on for which 0 <1. This can be illustrated by enumerating S = and associating with each subset T ⊆ S the k-tuple from k of which the ith coordinate is 1 if and only if si is a member of T
21.
Square lattice
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In mathematics, the square lattice is a type of lattice in a two-dimensional Euclidean space. It is the version of the integer lattice, denoted as Z2. It is one of the five types of two-dimensional lattices as classified by their groups, its symmetry group in IUC notation as p4m, Coxeter notation as. Two orientations of an image of the lattice are by far the most common and they can conveniently be referred to as the upright square lattice and diagonal square lattice, the latter is also called the centered square lattice. They differ by an angle of 45° and this is related to the fact that a square lattice can be partitioned into two square sub-lattices, as is evident in the colouring of a checkerboard. The square lattices symmetry category is wallpaper group p4m, a pattern with this lattice of translational symmetry cannot have more, but may have less symmetry than the lattice itself. An upright square lattice can be viewed as a square lattice with a mesh size that is √2 times as large. Correspondingly, after adding the centers of the squares of a square lattice we have a diagonal square lattice with a mesh size that is √2 times as small as that of the original lattice. A pattern with 4-fold rotational symmetry has a lattice of 4-fold rotocenters that is a factor √2 finer. With respect to reflection axes there are three possibilities, None, centered square number Euclids orchard Gaussian integer Hexagonal lattice Quincunx Square tiling
22.
Mathematical morphology
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Mathematical morphology is a theory and technique for the analysis and processing of geometrical structures, based on set theory, lattice theory, topology, and random functions. MM is most commonly applied to images, but it can be employed as well on graphs, surface meshes, solids. Topological and geometrical continuous-space concepts such as size, shape, convexity, connectivity, MM is also the foundation of morphological image processing, which consists of a set of operators that transform images according to the above characterizations. The basic morphological operators are erosion, dilation, opening and closing, MM was originally developed for binary images, and was later extended to grayscale functions and images. The subsequent generalization to complete lattices is widely accepted today as MMs theoretical foundation, Mathematical Morphology was developed 1964 by the collaborative work of Georges Matheron and Jean Serra, at the École des Mines de Paris, France. In 1968, the Centre de Morphologie Mathématique was founded by the École des Mines de Paris in Fontainebleau, France, led by Matheron, a random approach was also developed, based on novel image models. Most of the work in that period was developed in Fontainebleau, from the mid-1970s to mid-1980s, MM was generalized to grayscale functions and images as well. Besides extending the main concepts to functions, this generalization yielded new operators, such as morphological gradients, top-hat transform, in the 1980s and 1990s, MM gained a wider recognition, as research centers in several countries began to adopt and investigate the method. MM started to be applied to a number of imaging problems. In 1986, Serra further generalized MM, this time to a framework based on complete lattices. This generalization brought flexibility to the theory, enabling its application to a larger number of structures, including color images, video, graphs, meshes. At the same time, Matheron and Serra also formulated a theory for morphological filtering, the 1990s and 2000s also saw further theoretical advancements, including the concepts of connections and levelings. In 1993, the first International Symposium on Mathematical Morphology took place in Barcelona, introduction by Pierre Soille, in, pgs. Appendix A, The Centre de Morphologie Mathématique, an overview by Jean Serra, in, foreword in In binary morphology, an image is viewed as a subset of an Euclidean space R d or the integer grid Z d, for some dimension d. The basic idea in binary morphology is to probe an image with a simple, pre-defined shape and this simple probe is called the structuring element, and is itself a binary image. Here are some examples of widely used structuring elements, Let E = R2, B is a disk of radius r. Let E = Z2, B is a 3x3 square, Let E = Z2, B is the cross given by, B=. The basic operations are shift-invariant operators strongly related to Minkowski addition, Let E be a Euclidean space or an integer grid, and A a binary image in E
23.
Conway's Game of Life
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The Game of Life, also known simply as Life, is a cellular automaton devised by the British mathematician John Horton Conway in 1970. The game is a game, meaning that its evolution is determined by its initial state. One interacts with the Game of Life by creating an initial configuration and observing how it evolves, or, for advanced players, by creating patterns with particular properties. The universe of the Game of Life is an infinite two-dimensional orthogonal grid of square cells, every cell interacts with its eight neighbours, which are the cells that are horizontally, vertically, or diagonally adjacent. At each step in time, the transitions occur, Any live cell with fewer than two live neighbours dies, as if caused by underpopulation. Any live cell with two or three live neighbours lives on to the next generation, Any live cell with more than three live neighbours dies, as if by overpopulation. Any dead cell with three live neighbours becomes a live cell, as if by reproduction. The initial pattern constitutes the seed of the system, the rules continue to be applied repeatedly to create further generations. The Game of Life emerged as Conways successful attempt to drastically simplify von Neumanns ideas, the game made its first public appearance in the October 1970 issue of Scientific American, in Martin Gardners Mathematical Games column. Gardner wrote, The game made Conway instantly famous, but it opened up a whole new field of mathematical research. Because of Lifes analogies with the rise, fall and alterations of a society of living organisms, ever since its publication, Conways Game of Life has attracted much interest, because of the surprising ways in which the patterns can evolve. Life provides an example of emergence and self-organization, the game can also serve as a didactic analogy, used to convey the somewhat counterintuitive notion that design and organization can spontaneously emerge in the absence of a designer. The popularity of Conways Game of Life was helped by its coming into being just in time for a new generation of inexpensive computer access which were being released into the market. The game could be run for hours on these machines, which would otherwise have remained unused at night, in this respect, it foreshadowed the later popularity of computer-generated fractals. For many, Life was simply a challenge, a fun way to use otherwise wasted CPU cycles. For some, however, Life had more philosophical connotations and it developed a cult following through the 1970s and beyond, current developments have gone so far as to create theoretic emulations of computer systems within the confines of a Life board. Conway chose his rules carefully, after experimentation, to meet these criteria. There should exist small initial patterns with chaotic, unpredictable outcomes, There should be potential for von Neumann universal constructors
24.
Voronoi diagram
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In mathematics, a Voronoi diagram is a partitioning of a plane into regions based on distance to points in a specific subset of the plane. That set of points is specified beforehand, and for each seed there is a region consisting of all points closer to that seed than to any other. These regions are called Voronoi cells, the Voronoi diagram of a set of points is dual to its Delaunay triangulation. It is named after Georgy Voronoi, and is called a Voronoi tessellation, a Voronoi decomposition. Voronoi diagrams have practical and theoretical applications to a number of fields, mainly in science and technology. They are also known as Thiessen polygons, in the simplest case, shown in the first picture, we are given a finite set of points in the Euclidean plane. Each such cell is obtained from the intersection of half-spaces, the line segments of the Voronoi diagram are all the points in the plane that are equidistant to the two nearest sites. The Voronoi vertices are the points equidistant to three sites, let X be a metric space with distance function d. Let K be a set of indices and let k ∈ K be a tuple of nonempty subsets in the space X. In other words, if d = inf denotes the distance between the point x and the subset A, then R k = The Voronoi diagram is simply the tuple of cells k ∈ K. In principle some of the sites can intersect and even coincide, in addition, infinitely many sites are allowed in the definition, but again, in many cases only finitely many sites are considered. Sometimes the induced combinatorial structure is referred to as the Voronoi diagram, however, in general the Voronoi cells may not be convex or even connected. In the usual Euclidean space, we can rewrite the definition in usual terms. Each Voronoi polygon R k is associated with a generator point P k, let X be the set of all points in the Euclidean space. Let P1 be a point that generates its Voronoi region R1, P2 that generates R2, and P3 that generates R3, and so on. Then, as expressed by Tran et al all locations in the Voronoi polygon are closer to the point of that polygon than any other generator point in the Voronoi diagram in Euclidian plane. As a simple illustration, consider a group of shops in a city, suppose we want to estimate the number of customers of a given shop. With all else being equal, it is reasonable to assume that customers choose their preferred shop simply by distance considerations, they will go to the shop located nearest to them
25.
Otsu's method
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Consequently, Otsus method is roughly a one-dimensional, discrete analog of Fishers Discriminant Analysis. Otsus method is also related to the Jenks optimization method. The extension of the method to multi-level thresholding is referred to as the multi Otsu method. This idea yields an effective algorithm, level is the threshold for the image. Matlab have built-in functions graythresh and multithresh in Image Processing Toolbox which are implemented with Otsus method and Multi Otsus method, another approach with vectorized method The implementation has a little redundancy of computation. But since Otsu method is fast, the implementation is acceptable, while in some environment, since we employ vectorisation form, loop computation might be faster. This method can be converted to multi-threshold method, with architecture minimum heap—children labels. NB, The input argument total is the number of pixels in the given image, the input argument histogram is a 256-element histogram of a grayscale image different gray-levels. This function outputs the threshold for the image, Otsu’s method exhibits the relatively good performance if the histogram can be assumed to have bimodal distribution and assumed to possess a deep and sharp valley between two peaks. But if the area is small compared with the background area. Then the possibly incorrect threshold determined by Otsu’s method results in the segmentation error, there are many improvements focusing on different limitations for Otsus method. One famous and effective way is known as two-dimensional Otsus method, at each pixel, the average gray-level value of the neighborhood is calculated. Let the gray level of a given picture be divided into L values, then a pair is formed, the pixel gray level and the average of the neighborhood. Each pair belongs to a 2-dimensional bin, the total number of bins is obviously L × L. The s and t is obtained iteratively which is similar with one-dimensional Otsus method. The values of s and t are changed till we obtain the maximum of tr , however, even with the dynamic programming approach, 2d Otsus method still has large time complexity. Therefore, many researches have been done to reduce the computation cost, if summed area tables are used to build the 3 tables, sum over P_i_j, sum over i * P_i_j, and sum over j * P_i_j, then the runtime complexity is the maximum of. Note that if only coarse resolution is needed in terms of threshold, function inputs and output, hists is a 256 ×256 2D-histogram of grayscale value and neighborhood average grayscale value pair
26.
Watershed (image processing)
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In the study of image processing, a watershed is a transformation defined on a grayscale image. The name refers metaphorically to a watershed, or drainage divide. There are different technical definitions of a watershed, in graphs, watershed lines may be defined on the nodes, on the edges, or hybrid lines on both nodes and edges. Watersheds may also be defined in the continuous domain, there are also many different algorithms to compute watersheds. Watershed algorithm is used in image processing primarily for segmentation purposes, in geology, a watershed is a divide that separates adjacent catchment basins. The idea was introduced in 1979 by S. Beucher and C, the basic idea consisted of placing a water source in each regional minimum in the relief, to flood the entire relief from sources, and build barriers when different water sources meet. The resulting set of barriers constitutes a watershed by flooding, a number of improvements, collectively called Priority-Flood, have since been made to this algorithm. Intuitively, a drop of falling on a topographic relief flows towards the nearest minimum. The nearest minimum is that minimum which lies at the end of the path of steepest descent, in terms of topography, this occurs if the point lies in the catchment basin of that minimum. The previous definition does not verify this condition, intuitively, the watershed is a separation of the regional minima from which a drop of water can flow down towards distinct minima. A formalization of this idea was provided in for defining a watershed of an edge-weighted graph. S. Beucher and F. Meyer introduced an algorithmic inter-pixel implementation of the watershed method, label each minimum with a distinct label. Initialize a set S with the labeled nodes, extract from S a node x of minimal altitude F, that is to say F = min. Attribute the label of x to each non-labeled node y adjacent to x, repeat Step 2 until S is empty. Previous notions focus on catchment basins, but not to the separating line. The topological watershed was introduced by M. Couprie and G. Bertrand in 1997, different approaches may be employed to use the watershed principle for image segmentation. Local minima of the gradient of the image may be chosen as markers, in case an over-segmentation is produced. One of the most common watershed algorithms was introduced by F, the algorithm works on a gray scale image
27.
Edge detection
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Edge detection includes a variety of mathematical methods that aim at identifying points in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The points at which image brightness changes sharply are typically organized into a set of curved line segments termed edges, the same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a tool in image processing, machine vision and computer vision, particularly in the areas of feature detection. The purpose of detecting changes in image brightness is to capture important events. If the edge detection step is successful, the subsequent task of interpreting the information contents in the image may therefore be substantially simplified. However, it is not always possible to obtain such ideal edges from real life images of moderate complexity, edge detection is one of the fundamental steps in image processing, image analysis, image pattern recognition, and computer vision techniques. The edges extracted from an image of a three-dimensional scene can be classified as either viewpoint dependent or viewpoint independent. A viewpoint independent edge typically reflects inherent properties of the objects, such as surface markings. A viewpoint dependent edge may change as the viewpoint changes, and typically reflects the geometry of the scene, a typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line can be a number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore usually be one edge on each side of the line, although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are affected by one or several of the following effects, focal blur caused by a finite depth-of-field. Penumbral blur caused by shadows created by light sources of non-zero radius, thus, a one-dimensional image f which has exactly one edge placed at x =0 may be modeled as, f = I r − I l 2 + I l. At the left side of the edge, the intensity is I l = lim x → − ∞ f, the scale parameter σ is called the blur scale of the edge. Ideally this scale parameter should be adjusted based on the quality of image to avoid destroying true edges of the image, to illustrate why edge detection is not a trivial task, consider the problem of detecting edges in the following one-dimensional signal. Here, we may say that there should be an edge between the 4th and 5th pixels. Moreover, one could argue that this case is one in there are several edges. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple, there are many methods for edge detection, but most of them can be grouped into two categories, search-based and zero-crossing based
28.
TWAIN
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TWAIN is an applications programming interface and communications protocol that regulates communication between software and digital imaging devices, such as image scanners and digital cameras. TWAIN is not a protocol, it requires a driver called Data Source for each device. The design of TWAIN began in January 1991, the TWAIN group was originally launched in 1992 by several members of the imaging industry, with the intention of standardizing communication between image handling software and hardware. Review of the original TWAIN Developer’s Toolkit occurred from April,1991 through January,1992, the word TWAIN is not officially an acronym, but it is a backronym. The official website notes that the word TWAIN is from Kiplings The Ballad of East and West —. — reflecting the difficulty, at the time, of connecting scanners and personal computers. It was up-cased to TWAIN to make it more distinctive and this led people to believe it was an acronym, and then to a contest to come up with an expansion. None was selected, but the entry Technology Without an Interesting Name continues to haunt the standard, for example, the Encyclopedia of Information Technology lists Technology Without an Interesting Name as the official meaning of TWAIN. Board and associate members of the TWAIN Working Group include, Atalasoft, a Lexmark company Avision Dynamsoft Kodak Alaris Fujitsu Computer Products of America Visioneer, Inc
29.
Image scanner
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Commonly used in offices are variations of the desktop flatbed scanner where the document is placed on a glass window for scanning. Mechanically driven scanners that move the document are typically used for large-format documents, a rotary scanner, used for high-speed document scanning, is a type of drum scanner that uses a CCD array instead of a photomultiplier. Non-contact planetary scanners essentially photograph delicate books and documents, all these scanners produce two-dimensional images of subjects that are usually flat, but sometimes solid, 3D scanners produce information on the three-dimensional structure of solid objects. Digital cameras can be used for the same purposes as dedicated scanners, when compared to a true scanner, a camera image is subject to a degree of distortion, reflections, shadows, low contrast, and blur due to camera shake. Resolution is sufficient for less demanding applications, Digital cameras offer advantages of speed, portability and non-contact digitizing of thick documents without damaging the book spine. As of 2010 scanning technologies were combining 3D scanners with digital cameras to create full-color, in the biomedical research area, detection devices for DNA microarrays are called scanners as well. These scanners are high-resolution systems, similar to microscopes, the detection is done via CCD or a photomultiplier tube. Modern scanners are considered the successors of early telephotography and fax input devices and it used electromagnets to drive and synchronize movement of pendulums at the source and the distant location, to scan and reproduce images. It could transmit handwriting, signatures, or drawings within an area of up to 150 x 100mm, Édouard Belins Belinograph of 1913, scanned using a photocell and transmitted over ordinary phone lines, formed the basis for the AT&T Wirephoto service. In Europe, services similar to a wirephoto were called a Belino and it was used by news agencies from the 1920s to the mid-1990s, and consisted of a rotating drum with a single photodetector at a standard speed of 60 or 120 rpm. They send a linear analog AM signal through standard telephone lines to receptors. Color photos were sent as three separated RGB filtered images consecutively, but only for special events due to transmission costs, Drum scanners capture image information with photomultiplier tubes, rather than the charge-coupled device arrays found in flatbed scanners and inexpensive film scanners. Modern color drum scanners use three matched PMTs, which red, blue, and green light, respectively. Light from the artwork is split into separate red, blue. Photomultipliers offer superior dynamic range and for this reason drum scanners can extract more detail from very dark areas of a transparency than flatbed scanners using CCD sensors. The smaller dynamic range of the CCD sensors, versus photomultiplier tubes, can lead to loss of shadow detail, while mechanics vary by manufacturer, most drum scanners pass light from halogen lamps though a focusing system to illuminate both reflective and transmissive originals. The drum scanner gets its name from the clear acrylic cylinder, depending on size, it is possible to mount originals up to 20x28, but maximum size varies by manufacturer. One of the features of drum scanners is the ability to control sample area
30.
Digital camera
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A digital camera or digicam is a camera that produces digital images that can be stored in a computer, displayed on a screen and printed. Most cameras sold today are digital, and digital cameras are incorporated into many devices ranging from PDAs, Digital and movie cameras share an optical system, typically using a lens with a variable diaphragm to focus light onto an image pickup device. The diaphragm and shutter admit the correct amount of light to the imager, just as with film, however, unlike film cameras, digital cameras can display images on a screen immediately after being recorded, and store and delete images from memory. Many digital cameras can also record moving videos with sound, some digital cameras can crop and stitch pictures and perform other elementary image editing. The history of the camera began with Eugene F. Lally of the Jet Propulsion Laboratory. His 1961 idea was to take pictures of the planets and stars while travelling through space to give information about the astronauts position, unfortunately, as with Texas Instruments employee Willis Adcocks filmless camera in 1972, the technology had yet to catch up with the concept. Steven Sasson as an engineer at Eastman Kodak invented and built the first electronic camera using a charge-coupled device image sensor in 1975, earlier ones used a camera tube, later ones digitized the signal. Early uses were military and scientific, followed by medical. In the mid to late 1990s digital cameras became common among consumers, by the mid-2000s digital cameras had largely replaced film cameras, and higher-end cell phones had an integrated digital camera. By the beginning of the 2010s, almost all smartphones had a digital camera. The two major types of image sensor are CCD and CMOS. A CCD sensor has one amplifier for all the pixels, while each pixel in a CMOS active-pixel sensor has its own amplifier, compared to CCDs, CMOS sensors use less power. Cameras with a small sensor use a back-side-illuminated CMOS sensor, overall final image quality is more dependent on the image processing capability of the camera, than on sensor type. The resolution of a camera is often limited by the image sensor that turns light into that discrete signals. The brighter the image at a point on the sensor. Depending on the structure of the sensor, a color filter array may be used. The number of pixels in the sensor determines the cameras pixel count, in a typical sensor, the pixel count is the product of the number of rows and the number of columns. For example, a 1,000 by 1,000 pixel sensor would have 1,000,000 pixels, final quality of an image depends on all optical transformations in the chain of producing the image
31.
Vanilla (computing)
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Vanilla software has become a widespread de facto industry standard, widely used by businesses and individuals. The term comes from the standard flavor of ice cream. According to Eric S. Raymonds The New Hackers Dictionary, vanilla means more default than ordinary, the term vanilla is sometimes also used for hardware components. For instance, in the 1990s non-upgraded Amiga home computers were called vanilla, similarly, for Unix-based kernels, a vanilla kernel refers to a kernel that has been unmodified by any third-party source. For instance, the vanilla Linux kernel is given a Linux distribution–specific flavour by being heavily modified. In PC games, the vanilla is often used to describe the original version of a game. It can also refer to the game engine when source ports or expansion packs are available. For example, World of Warcraft could refer to either the game or one of the expansion packs. In his book End of Ignorance, Charles Winborne refers to a page that is ″only a text file. Commercial off-the-shelf Out of the box Plain vanilla Turnkey
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Halftone
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Halftone is the reprographic technique that simulates continuous tone imagery through the use of dots, varying either in size or in spacing, thus generating a gradient-like effect. Halftone can also be used to specifically to the image that is produced by this process. This reproduction relies on an optical illusion, the tiny halftone dots are blended into smooth tones by the human eye. At a microscopic level, developed black-and-white photographic film also consists of two colors, and not an infinite range of continuous tones. The semi-opaque property of ink allows halftone dots of different colors to create another optical effect—full-color imagery, william Fox Talbot is credited with the idea of halftone printing. In a 1852 patent he suggested using photographic screens or veils in connection with an intaglio process. Several different kinds of screens were proposed during the following decades, one of the well known attempts was by Stephen H. Horgan while working for the New York Daily Graphic. The first printed photograph was an image of Steinway Hall in Manhattan published on December 2,1873, the Graphic then published the first reproduction of a photograph with a full tonal range in a newspaper on March 4,1880 with a crude halftone screen. The first truly successful commercial method was patented by Frederic Ives of Philadelphia in 1881, although he found a way of breaking up the image into dots of varying sizes, he did not make use of a screen. In 1882, the German Georg Meisenbach patented a process in England. His invention was based on the ideas of Berchtold and Swan. He used single lined screens which were turned during exposure to produce cross-lined effects and he was the first to achieve any commercial success with relief halftones. Shortly afterwards, Ives, this time in collaboration with Louis and Max Levy, improved the process further with the invention, the relief halftone process proved almost immediately to be a success. The use of blocks in popular journals became regular during the early 1890s. The development of printing methods for lithography appears to have followed a largely independent path. In the 1860s, A. Hoen & Co. focused on methods allowing artists to manipulate the tones of hand-worked printing stones, by the 1880s, Hoen was working on halftone methods that could be used in conjunction with either hand-worked or photolithographic stones. The most common method of creating screens—amplitude modulation—produces a regular grid of dots that vary in size, the other method of creating screens—frequency modulation—is used in a process also known as stochastic screening. Both modulation methods are named by analogy with the use of the terms in telecommunications, the resolution of a halftone screen is measured in lines per inch
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Oversampled binary image sensor
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An oversampled binary image sensor is a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a response, giving only a one-bit quantized measurement of the local light intensity. The response function of the sensor is non-linear and similar to a logarithmic function. Before the advent of digital sensors, photography, for the most part of its history. At the heart of every photographic film are a number of light-sensitive grains of silver-halide crystals. During exposure, each micron-sized grain has a fate, Either it is struck by some incident photons and becomes exposed, or it is missed by the photon bombardment. Thus, in essence, photographic film is a binary imaging medium, thanks to the small size and large number of these grains, one hardly notices this quantized nature of film when viewing it at a distance, observing only a continuous gray tone. The oversampled binary image sensor is reminiscent of photographic film, each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. At the start of the period, all pixels are set to 0. A pixel is set to 1 if the number of photons reaching it during the exposure is at least equal to a given threshold q. One way to build such binary sensors is to standard memory chip technology. With current CMOS technology, the level of integration of systems can exceed 109~1010 pixels per chip. In this case, the pixel sizes are far below the diffraction limit of light. Intuitively, one can exploit this spatial redundancy to compensate for the loss due to one-bit quantizations. Building a binary sensor that emulates the photographic process was first envisioned by Fossum. The original motivation was mainly out of technical necessity, the miniaturization of camera systems calls for the continuous shrinking of pixel sizes. At a certain point, however, the limited capacity of small pixels becomes a bottleneck, yielding very low signal-to-noise ratios. Consider a simplified camera model shown in Fig.1, the λ0 is the incoming light intensity field
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Black and white
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Black and white, often abbreviated B/W or B&W, and hyphenated black-and-white when used as an adjective, is any of several monochrome forms in visual arts. Black-and-white images are not usually starkly contrasted black and white and they combine black and white in a continuum producing a range of shades of gray. Even when most studios had the capability to make color films they were not heavily utilized as using the Technicolor process was expensive, for the years 1940–1966, a separate Academy Award for Best Art Direction was given for black-and-white movies along with one for color. Television, Television program was first transmitted in black-and-white, scottish inventor John Logie Baird demonstrated the worlds first color television transmission on July 3,1928 using a mechanical process. Some color broadcasts in the US began in the 1950s, with color becoming common in industrialized nations during the late 1960s. In the United States, the Federal Communications Commission settled on a color NTSC standard in 1953, color television became more widespread in the U. S. between 1963 and 1967, when the CBS and ABC networks joined NBC in broadcasting full color schedules. Canada began airing color television in 1966 while the United Kingdom began to use a different color system from July 1967 known as PAL. The Republic Of Ireland followed in 1970, in China, black and white television sets were the norm until as late as the 1990s, color TVs not outselling them until about 1989. While seldom used now, many consumer camcorders have the ability to record in black-and-white. Photography, Photographs were either black-and-white or shades of sepia, color photography was originally rare and expensive and again often containing inaccurate hues. Color photography became more common from the mid-20th century, Today, black-and-white is a niche market for photographers who use the medium for artistic purposes. This can take the form of film or digital conversion to grayscale. For amateur use certain companies such as Kodak manufactured black-and-white disposable cameras until 2009, also, certain films are produced today which give black-and-white images using the ubiquitous C41 color process. Printing press, Most American newspapers were black-and-white until the early 1980s, The New York Times, some claim that USA Today was the major impetus for the change to color. In the UK, color was only introduced from the mid-1980s. Even today, many newspapers restrict color photographs to the front, similarly, daily comic strips in newspapers were traditionally black-and-white with color reserved for Sunday strips. Sometimes color is reserved for the cover, magazines such as Jet magazine were either all or mostly black-and-white until the end of the 2000s when it became all-color. Manga are typically published in black-and-white although now it is part of its image, many school yearbooks are still entirely or mostly in black-and-white
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Connected-component labeling
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Connected-component labeling is an algorithmic application of graph theory, where subsets of connected components are uniquely labeled based on a given heuristic. Connected-component labeling is not to be confused with segmentation, Connected-component labeling is used in computer vision to detect connected regions in binary digital images, although color images and data with higher dimensionality can also be processed. When integrated into a recognition system or human-computer interaction interface. Blob extraction is performed on the resulting binary image from a thresholding step. Blobs may be counted, filtered, and tracked, Blob extraction is related to but distinct from blob detection. A graph, containing vertices and connecting edges, is constructed from relevant input data, the vertices contain information required by the comparison heuristic, while the edges indicate connected neighbors. An algorithm traverses the graph, labeling the vertices based on the connectivity, connectivity is determined by the medium, image graphs, for example, can be 4-connected or 8-connected. Following the labeling stage, the graph may be partitioned into subsets, the algorithms discussed can be generalized to arbitrary dimensions, albeit with increased time and space complexity. This is a fast and very simple method to implement and understand and it is based on graph traversal methods in graph theory. In short, once the first pixel of a component is found. This algorithm is part of Vincent and Soilles watershed segmentation algorithm, in order to do that a linked list is formed that will keep the indexes of the pixels that are connected to each other, steps and below. The method of defining the linked list specifies the use of a depth or a breadth first search, for this particular application, there is no difference which strategy to use. The simplest kind of a last in first out queue implemented as a linked list will result in a depth first search strategy. It is assumed that the image is a binary image, with pixels being either background or foreground. The algorithm steps can be written as, Start from the first pixel in the image, if this pixel is a foreground pixel and it is not already labelled, then give it the label curlab and add it as the first element in a queue, then go to. If it is a pixel or it was already labelled. Pop out an element from the queue, and look at its neighbours, if a neighbour is a foreground pixel and is not already labelled, give it the curlab label and add it to the queue. Repeat until there are no elements in the queue
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Discrete tomography
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Discrete tomography focuses on the problem of reconstruction of binary images from a small number of their projections. In general, tomography deals with the problem of determining shape, from the mathematical point of view, the object corresponds to a function and the problem posed is to reconstruct this function from its integrals or sums over subsets of its domain. In general, the inversion problem may be continuous or discrete. In continuous tomography both the domain and the range of the function are continuous and line integrals are used, in discrete tomography the domain of the function may be either discrete or continuous, and the range of the function is a finite set of real, usually nonnegative numbers. In continuous tomography when a number of projections is available. It is typical for discrete tomography that only a few projections are used, in this case, conventional techniques all fail. A special case of discrete tomography deals with the problem of the reconstruction of an image from a small number of projections. The name discrete tomography is due to Larry Shepp, who organized the first meeting devoted to this topic, discrete tomography has strong connections with other mathematical fields, such as number theory, discrete mathematics, complexity theory and combinatorics. In fact, a number of discrete tomography problems were first discussed as combinatorial problems, in 1957, H. J. Ryser found a necessary and sufficient condition for a pair of vectors being the two orthogonal projections of a discrete set. In the proof of his theorem, Ryser also described a reconstruction algorithm, in the same year, David Gale found the same consistency conditions, but in connection with the network flow problem. Another result of Rysers is the definition of the operation by which discrete sets having the same projections can be transformed into each other. The problem of reconstructing an image from a small number of projections generally leads to a large number of solutions. Reconstructing planar lattice sets from their 1-dimensional X-rays is an NP-hard problem if the X-rays are taken from m ≥3 lattice directions, the reconstruction problem is highly unstable for m ≥3 and stable for m =2, see. Coloring a grid using k colors with the restriction that each row, the problem is NP-hard for k ≥3, see. Among the reconstruction methods one can find algebraic reconstruction techniques, greedy algorithms, a form of discrete tomography also forms the basis of nonograms, a type of logic puzzle in which information about the rows and columns of a digital image is used to reconstruct the image
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Netpbm format
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A Netpbm format is any graphics format used and defined by the Netpbm project. The portable pixmap format, the portable graymap format and the portable bitmap format are image file formats designed to be exchanged between platforms. They are also referred to collectively as the portable anymap format. Poskanzer developed the first library of tools to handle the PBM format, Pbmplus and it mainly contained tools to convert between PBM and other graphics formats. By the end of 1988, Poskanzer had developed the PGM and PPM formats along with their associated tools, the final release of Pbmplus was December 10,1991. In 1993, the Netpbm library was developed to replace the unmaintained Pbmplus and it was simply a repackaging of Pbmplus with additions and fixes submitted by people all over the world. Each file starts with a magic number that identifies the type of file it is. The magic number is a capital P followed by a single-digit number, a value of P7 refers to the PAM file format that is covered as well by the netpbm library. The ASCII formats allow for human readability and easy transfer to other platforms, the binary formats are more efficient in file size but may have native byte-order issues. In the binary formats, PBM uses 1 bit per pixel, PGM uses 8 bits per pixel, the hash sign introduces a comment. The next two numbers give the width and the height, then follows the matrix with the pixel values. Here is the image, Here it is again magnified 20 times, Note that a 0 signifies a white pixel. This is in contrast to the formats, where higher values signify brighter pixels. The P4 binary format of the image represents each pixel with a single bit, packing 8 pixels per byte. Extra bits are added at the end of each row to fill a whole byte, the PGM and PPM formats have an additional parameter for the maximum value after the X and Y dimensions and before the actual pixel data. Black is 0 and max value is white, there is a newline character at the end of each line. There is a character at the end of each line. The file is smaller, but the information is difficult to read by humans
38.
JBIG2
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JBIG2 is an image compression standard for bi-level images, developed by the Joint Bi-level Image Experts Group. It is suitable for both lossless and lossy compression, JBIG2 has been published in 2000 as the international standard ITU T.88, and in 2001 as ISO/IEC14492. Ideally, a JBIG2 encoder will segment the input page into regions of text, regions of halftone images, regions that are neither text nor halftones are typically compressed using a context-dependent arithmetic coding algorithm called the MQ coder. Textual regions are compressed as follows, the pixels in the regions are grouped into symbols. A dictionary of symbols is then created and encoded, typically also using context-dependent arithmetic coding, typically, a symbol will correspond to a character of text, but this is not required by the compression method. Halftone images may be compressed by reconstructing the image used to generate the halftone. Overall, the used by JBIG2 to compress text is very similar to the JB2 compression scheme used in the DjVu file format for coding binary images. PDF files versions 1.4 and above may contain JBIG2-compressed data, open-source decoders for JBIG2 are jbig2dec, the java-based jbig2-imageio and the decoder found in versions 2.00 and above of xpdf. Typically, an image consists mainly of a large amount of textual and halftone data. The bi-level image is segmented into three regions, text, halftone, and generic regions, each region is coded differently and the coding methodologies are described in the following passage. Text coding is based on the nature of human visual interpretation, a human observer cannot tell the difference between two instances of the same characters in a bi-level image even though they may not exactly match pixel by pixel. Therefore, only the bitmap of one representative character instance needs to be coded instead of coding the bitmaps of each occurrence of the same character individually, for each character instance, the coded instance of the character is then stored into a symbol dictionary. There are two encoding methods for image data, pattern matching and substitution and soft pattern matching. These methods are presented in the following subsections, the position is usually relative to another previously coded character. If a match is not found, the segmented pixel block is coded directly, typical procedures of pattern matching and substitution algorithm are displayed in the left block diagram of the figure above. Although the method of PM&S can achieve outstanding compression, substitution errors could be made during the process if the resolution is low. The deployment of refinement data can make the character-substitution error mentioned earlier highly unlikely, the refinement data contains the current desired character instance, which is coded using the pixels of both the current character and the matching character in the dictionary. Since it is known that the current character instance is highly correlated with the matched character, halftone images can be compressed using two methods
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X BitMap
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In computer graphics, the X Window System used X BitMap, a plain text binary image format, for storing cursor and icon bitmaps used in the X GUI. The XBM format was replaced by XPM for X11 in 1989, XBM files differed markedly from most image files in that they took the form of C source files. This meant that they could be compiled directly into an application without any preprocessing steps, XBM data typically appeared in headers and consisted of a series of static unsigned char arrays containing the monochrome pixel data. They featured one array per image stored in the header, the following piece of C code exemplifies an XBM file, In place of the usual image-file-format header, XBM files had two or four #define statements. The first two #defines specified the height and width of the bitmap in pixels, the second two, if they existed, specified the position of any hotspot within the bitmap. The image data consisted of a line of pixel values stored in a static array, if the image width did not match a multiple of 8, the display mechanism ignored and discarded the extra bits in the last byte of each row. Some web browsers support displaying XBM images as a holdover from the days of the world wide web. The Arena web browser had full support since version 0.3.34, XBM support was removed from Internet Explorer 6, Mozilla Firefox 3.6, and WebKit-based browsers. There is an indication that Chromium does not support XBM. Documentation for Opera 2.12 and 6.0 indicates that XBM was at least previously supported, some image viewers/converters, e. g. XnView, FFmpeg and IrfanView, still support XBM. A 48×48 XBM can be converted to Ikon and eventually X-Face with Netpbm tools, XBM is also used in embedded processing to display Icons used in GUIs. X PixMap Xlib Portable Bit Map
40.
International Standard Book Number
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The International Standard Book Number is a unique numeric commercial book identifier. An ISBN is assigned to each edition and variation of a book, for example, an e-book, a paperback and a hardcover edition of the same book would each have a different ISBN. The ISBN is 13 digits long if assigned on or after 1 January 2007, the method of assigning an ISBN is nation-based and varies from country to country, often depending on how large the publishing industry is within a country. The initial ISBN configuration of recognition was generated in 1967 based upon the 9-digit Standard Book Numbering created in 1966, the 10-digit ISBN format was developed by the International Organization for Standardization and was published in 1970 as international standard ISO2108. Occasionally, a book may appear without a printed ISBN if it is printed privately or the author does not follow the usual ISBN procedure, however, this can be rectified later. Another identifier, the International Standard Serial Number, identifies periodical publications such as magazines, the ISBN configuration of recognition was generated in 1967 in the United Kingdom by David Whitaker and in 1968 in the US by Emery Koltay. The 10-digit ISBN format was developed by the International Organization for Standardization and was published in 1970 as international standard ISO2108, the United Kingdom continued to use the 9-digit SBN code until 1974. The ISO on-line facility only refers back to 1978, an SBN may be converted to an ISBN by prefixing the digit 0. For example, the edition of Mr. J. G. Reeder Returns, published by Hodder in 1965, has SBN340013818 -340 indicating the publisher,01381 their serial number. This can be converted to ISBN 0-340-01381-8, the check digit does not need to be re-calculated, since 1 January 2007, ISBNs have contained 13 digits, a format that is compatible with Bookland European Article Number EAN-13s. An ISBN is assigned to each edition and variation of a book, for example, an ebook, a paperback, and a hardcover edition of the same book would each have a different ISBN. The ISBN is 13 digits long if assigned on or after 1 January 2007, a 13-digit ISBN can be separated into its parts, and when this is done it is customary to separate the parts with hyphens or spaces. Separating the parts of a 10-digit ISBN is also done with either hyphens or spaces, figuring out how to correctly separate a given ISBN number is complicated, because most of the parts do not use a fixed number of digits. ISBN issuance is country-specific, in that ISBNs are issued by the ISBN registration agency that is responsible for country or territory regardless of the publication language. Some ISBN registration agencies are based in national libraries or within ministries of culture, in other cases, the ISBN registration service is provided by organisations such as bibliographic data providers that are not government funded. In Canada, ISBNs are issued at no cost with the purpose of encouraging Canadian culture. In the United Kingdom, United States, and some countries, where the service is provided by non-government-funded organisations. Australia, ISBNs are issued by the library services agency Thorpe-Bowker
