Reading System for Color Tag Based on Machine Vision
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Color Tag indicates a special meaning, which is usually used to recognise and classify products in pipelining occasion. As the time of modern roboticized industry comes, quondam homochromous Color Tag is too simple to satisfy double-quick industrial demand. As a result, the technique of judgement of series Color Tag has its naissance. The technique comes true here via programing with Visual Basic. In order to recognise it, we orientate the Color Tag, distill the color and contrast one color with another. For example, we can figure out the value of color-ringed resistance by the technique. At first, wo input a picture of the resistance. The programme itself will tell us the direction, the rings and their color, then it calculates the value of the resistance by a special formula. The designment is excellent because it is convenient to use widely and it recognises quickly.Keywords:
Value (mathematics)
Judgement
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Humans see the world in colors. When it comes to the aspect of just looking, all it does is please the eyes but when it comes to questioning its make, it becomes a challenge. It is much easier to be served the values without the tedious task of finding a person who understands colors. This paper proposes the idea of teaching a computer to detect and define a color well enough to have useful applications. The detection algorithm proposed uses the advantage of the camera and fed in data to detect even the color based on RGB values. The algorithm involved calls on a function that runs loops on readjusting the distance based on a nearest match. This effortlessly helps define a color based on the RGB color space with a peaking accuracy.
RGB color model
Color balance
HSL and HSV
RGB color space
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This paper represents a critical review of some of the color processing in the consumer TV processing chain. As such, a default processing chain is assumed as a starting point. The flow of color image information through this chain is described and critiqued. That is followed by development and description of some “clean slate” theoretical approaches to video processing with color accuracy and quality as the highest priority. These two approaches are compared and contrasted to provide some practical insight into how color science could be used in a practical sense to improve consumer video processing. Additionally, some examples how color and image appearance models might be used in the development of consumer video systems are described. 1. COLORIMETRY OF IMAGING SYSTEMS Perhaps it is no coincidence that standardized methods of colorimetry have developed in parallel with various forms of commercially viable color imaging systems over the past century. As a reference point one could look at the establishment of the CIE 1931 Standard Colorimetric Observer only a few years before the invention of Kodachrome film. In fact, David Wright, one of the fathers of CIE colorimetry and an early researcher on color television, pointed out that had CIE colorimetry not existed prior to the development of television, it would have had to be invented.[1] Color measurement in imaging systems can be organized into a three-level hierarchy from devicedependent metrics through device-independent techniques to the viewing-conditions-independent methods of color appearance models.[2] Device-dependent metrics are those that define the amount of color signal in a given imaging device without defining the meaning of those signals outside the particular device being used (e.g., CMYK dye amounts or RGB digital counts). Device-independent metrics express the image information in terms of colorimetric coordinates such as XYZ or CIELAB or device coordinates such as RGB or CMYK combined with colorimetric definitions of the meaning of those device coordinates (i.e., a device definition, characterization, or profile). Viewing-condition-independent specifications recognize that the color appearance of any given scene or reproduction will depend on viewing conditions such as luminance level, surround, chromatic adaptation, etc. and attempt to specify final image appearance. In consumer video applications, the framework is present for some forms of device-independent color imaging, but actual implementations are not controlled to the degree necessary and, unfortunately, much of the consumer video world remains in the domain of devicedependency. 2. OBJECTIVES IN COLOR REPRODUCTION Hunt[3] defined six objectives in color reproduction and also reviewed them in his reference work on the reproduction of color.[4] These objectives are 1. spectral, 2. colorimetric, 3. exact, 4. equivalent, 5. corresponding, and 6. preferred color reproduction. While full description of these objectives is not possible in this short paper, they do provide the necessary theoretical framework for understanding the requirements for evolution of color reproduction from device-dependent to viewing-conditionsdependent systems. Consumer video does not presently achieve any of Hunt’s objectives, but it might be said that it is aiming for preferred color reproduction. However, as Hunt[3] pointed out, the first five objectives “provide a framework which is a necessary preliminary to any discussion of deliberate distortions of colour reproduction.” Fairchild[2] has taken a slightly different approach to this question by defining five levels of color reproduction that cast Hunt’s objectives into a different hierarchy. These levels are 1. color reproduction, 2. pleasing color reproduction, 3. colorimetric color reproduction, 4. color appearance reproduction, and 5. color preference reproduction. In this format, each successive level depends on the previous levels being achieved first. Consumer video systems are currently functioning at levels 1 and 2. The following sections attempt to describe some possibilities for advancing to a higher level. 3. DISPLAY-CENTRIC VIDEO Essentially from the moment of image capture, color in video is defined by some form of standardized display. For example, the encoded video signal (e.g. Y’CBCR) is defined by the display primaries, transfer function, and scaling.[5] Such a system can be sufficient to implement accurate device-independent color imaging for the displayed content. It could, but apparently is not, also be used for device-independent video capture if the cameras were colorimetrically characterized and the signal then encoded directly in display-centric characterized RGB (or a known transform thereof). However, the capture end of the system is rarely implemented in a colorimetrically-accurate method. Instead, the controls available to the videographer are used to capture “pleasing” images encoded for the chosen standard display rather than accurate measurements. For some color reproduction objectives this is perfectly adequate and appropriate, but for others accurate color information about the scene might well be lost at the very first step of the imaging chain. One is then left with processing and enhancing information for the display. This display-centric (sometimes called output-referred) bias of video systems is not necessarily a flaw, but it needs to be recognized that the processing of video color is focused on the display rather than the original scene (a scene-centric, or scene-referred approach). Once the display-centric nature of video systems is recognized, the task becomes one of properly interpreting the colorimetric meaning of the video signals in order to do appropriate video processing (e.g., differential processing of luminance and chromatic information or colorimetric transformations to displays that do not match the nominal standard display). Unfortunately, notations such as YCBCR are significantly overloaded and it is often difficult to determine just what the quantities represent (not to mention that there are other similar transformations with ill-defined names like YUV). Are the quantities linear or “gamma corrected”? If nonlinear, which transfer function was used? What primary set was used to define RGB? What coefficients were used to define the luminance transform? Is it even luminance? How are the quantities scaled and quantized? The questions seem endless and unless the image data encoding is accompanied with some definition of these variables, there is no chance for accurate colorimetry and meaningful perceptual processing of content. As Poynton[5] summarized, “the existence of several standard sets of primary chromaticities, the introduction of new coefficients, and continuing confusion between luminance and luma all beg for a notation to distinguish among the many possible combinations.” Given the potential for confusion (and the reality), how is it possible that acceptable results are obtained at all? The answer probably lies in the reality that very few, if any, displays match a given encoding standard, they are adjusted differently by users, and the viewing conditions have a significant impact on image appearance. Again, the best that can be done is to be aware of the potential for confusion, minimize it, and process the color information appropriately given what is available.
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A display’s color subpixel geometry provides an intriguing opportunity for improving readability of text. True type fonts can be positioned at the precision of subpixel resolution. With such a constraint in mind, how does one need to design font characteristics? On the other hand, display manufactures try hard in addressing the color display’s dilemma: smaller pixel pitch and larger display diagonals strongly increase the total number of pixels. Consequently, cost of column and row drivers as well as power consumption increase. Perceptual color subpixel rendering using color component subsampling may save about 1/3 of color subpixels (and reduce power dissipation). This talk will try to elaborate the following questions, based on simulation of several different layouts of subpixel matrices: Up to what level are display device constraints compatible with software specific ideas of rendering text? How much of color contrast will remain? How to best consider preferred viewing distance for readability of text? How much does visual acuity vary at 20/20 vision? Can simplified models of human visual color perception be easily applied to text rendering on displays? How linear is human visual contrast perception around band limit of a display’s spatial resolution? How colorful does the rendered text appear on the screen? How much does viewing angle influence the performance of subpixel layouts and color subpixel rendering?
Subpixel rendering
Primary color
Dither
Color quantization
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Industrial vision systems are often hindered by system, irregularities such as vibration, product shift and non- constant speed (either conveyor or product) which will set criteria for selecting color detector for machine vision systems. Color line scan camera offers a new tool for industrial process and quality control applications having the benefits of traditional monochrome technology for accurate dimension, shape and texture detection with addition of new intensity independent dimension-color. In some cases color is the only reliable feature. The color separation is the most essential part of a good color cameras. If the three different colors can not be separated successfully from each other, the system can only detect clear and obvious color differences. Separating the image into different spectral bandwidth s increases the intensities dynamic range for each channel compared to a monochrome image. The CCD cameras has to be able to measure the colors accurately even if the light levels are of a small magnitude since correcting erroneous data caused by poor dynamic response from the camera is almost impossible. Often more digitized levels per pixel are needed to deal with in a color image compared with a monochrome image. Good dynamic range and linear response is necessary for color machine vision. The importance of these features becomes even more important when the image is converted to another color space. Some information will always be lost when converting integer data to another form. If the numbers used for conversion are too small, the calculation can have an error that is huge enough to make the system fail. Color machine vision has shown a dynamic uptrend in use within the past few years as the introduction of new cameras and scanner technologies itself underscores. In the future, the movement from monochrome imaging to color will hasten, as machine vision system users demand more knowledge about their product stream.
Monochrome
Color depth
Color balance
ICC profile
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Color Recognition is a technology which is used to identify human colors with the help of mathematical algorithm with which we recognize the object, tracks the object movements & also provides information about object position orientation and flux of the objects by using the Optical Color Recognition (OCR) Algorithm. In this paper, the color markers are placed at the tip of the objects. This helps the webcam to identify the movement of object and the color recognition. The drawing application allows the user, you draw on any surface by tracking the object movements of the user's index object which can be stored and replaced on any other surface. The user can even shamble through numerous footage and drawing by exploitation the item color movements. We solely ought to absorb and amalgamate the benefits of varied art forms so as to create more marvelous works of art that facilitate the audience to obtain and acknowledge.
Tracking (education)
Position (finance)
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When it comes to total color blindness, it is very hard for a regular, color-seeing people to imagine the world, where the color information is missing. Because we can not see through the eyes of the other person, we can only assume based on feedback and biological studies. There had been numerous studies with the goal to create the simulation of color blindness, but as we explain in this paper, most of them are not accurate. Moreover, with the help of participants suffering from achromatopsia (one form of total color blindness), we propose our technique for simulating this phenomenon.
The basis of this research is that if a perfect simulation exists, achromatopic (i.e. totally colorblind) person should not distinguish between a normal, colored picture and a grayscaled version of it [1]. This is also the argument, why we think that the current approaches to this problem do not produce correct results – the subjects see a marginal difference and the pictures based on such algorithms look very unnatural to them.
To harness this idea, we have created a simple computer program, which can calibrate the color-to-grayscale algorithm based on the input from the colorblind person. The calibrating phase consists of a series of tests in which the application goes through a set of colors (red, green, blue, etc.) and the subject has to match the provided color with a shade of gray that, from his point of view, corresponds to that color. The data is then fed to an algorithm (weighted average with non-linear weights) that creates the simulation. It can be then applied to either pictures, or visualized on a RGB cube. This particular way of describing colorblindness has advantages, such as selecting the regions that look uniform to an achromat – an area that has constant gray levels (slice of the cube).
We believe that this research will lead to eradication of false and not accurate simulations of colorblindness. Also, it will be great if it can spark more interest in the study of color vision disabilities.
!!Acknowledgments
Special thanks to my tutor RNDr. Kristina Rebrova PhD. for her advice, guidance and valuable insight into the topic of color vision.
!!References
[1] M. Osrman, Assistive software for people with color vision disabilities (in Slovak), Bc. thesis, FMFI, Comenius Univ., Bratislava, Slovakia, 2014.
Gray (unit)
Colored
Color difference
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As an introduction to the session on color vision and multisensor processing, we review the physics of color imaging as well as fundamentals of human color perception. Alternative color representations motivated by both pattern classification and visual perception are discussed, as are classes of color algorithms used in scene segmentation.
Color normalization
Color correction
False color
Imaging science
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Color image quantization is a strategy in which a smaller number of colors are used to represent the image. The objective is to make the quality approximate as closely to the original true-color image. The technology is widely used in non-true-color displays and in color printers that cannot reproduce a large number of different colors. However, the main problem the quantization of color image has to face is how to use less colors to show the color image. Therefore, it is very important to choose one suitable palette for an index color image. In this paper, we shall propose a new approach which employs the concept of Multi-Dimensional Directory (MDD) together with the one cycle LBG algorithm to create a high-quality index color image. Compared with the approaches such as VQ, ISQ, and Photoshop v.5, our approach can not only acquire high quality image but also shorten the operation time.
Color quantization
Dither
High color
Color depth
Palette (painting)
Color histogram
Color correction
Color balance
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Color machine vision has shown a dynamic uptrend in use within the past few years as the introduction of new cameras and scanner technologies itself underscores. In the future, the movement from monochrome imaging to color will hasten, as machine vision system users demand more knowledge about their product stream. As color has come to the machine vision, certain requirements for the equipment used to digitize color images are needed. Color machine vision needs not only a good color separation but also a high dynamic range and a good linear response from the camera used. Good dynamic range and linear response is necessary for color machine vision. The importance of these features becomes even more important when the image is converted to another color space. There is always lost some information when converting integer data to another form. Traditionally the color image processing has been much slower technique than the gray level image processing due to the three times greater data amount per image. The same has applied for the three times more memory needed. The advancements in computers, memory and processing units has made it possible to handle even large color images today cost efficiently. In some cases he image analysis in color images can in fact even be easier and faster than with a similar gray level image because of more information per pixel. Color machine vision sets new requirements for lighting, too. High intensity and white color light is required in order to acquire good images for further image processing or analysis. New development in lighting technology is bringing eventually solutions for color imaging.
Color balance
Machine Vision
ICC profile
Color management
Color depth
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