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Fundamentals of image analysis and interpretation
Published in Michael O’Byrne, Bidisha Ghosh, Franck Schoefs, Vikram Pakrashi, Image-Based Damage Assessment for Underwater Inspections, 2019
Bidisha Ghosh, Michael O’Byrne, Franck Schoefs, Vikram Pakrashi
There are various ways that color can be represented. Humans may describe a color by its attributes of brightness, hue and, colorfulness, a printing press may produce a specific color based on the reflectance and absorbance of cyan, magenta, yellow, and black inks on the printing paper, while an LCD monitor or TV screen may render a color based on the intensity of the red, green, and blue subpixels at each pixel location. A color space, also known as a color model, is a specific organization of colors that allows color information to be represented numerically. Generally, it is observed that a color can be specified using three coordinates, or parameters. These parameters describe the position of the color within the color space and are sometimes referred to as the “tristimulus values.” There is a vast array of color spaces; popular ones in the domain of image processing include RGB, HSV, and L*a*b*. There are many more color spaces that are very similar to these color spaces.
Disease-Inspired Feature Design for Computer-Aided Diagnosis of Breast Cancer Digital Pathology Images
Published in de Azevedo-Marques Paulo Mazzoncini, Mencattini Arianna, Salmeri Marcello, Rangayyan Rangaraj M., Medical Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy, 2018
Color image data can be transformed between color spaces using linear transformation matrices. One useful color space considered in this work is the HSV (hue, saturation, value) space. Value, or brightness, is the proximity of the color to black or white, along the line from [0,0,0]T to [L,L,L]T in the RGB space. Hue is the dominant color component, or the polar angle around the value line, while saturation is the intensity of the color, or the magnitude of the polar arm stemming from the value line. Another useful space that is used in this work is the L*a*b* space, comprising of a similar brightness dimension L*, and two color dimensions a* and b*, spanning red to green, and yellow to blue, respectively. The L*a*b* space was designed to cover a wider range of perceptible colors, is a perceptually uniform (linear) color space, and is device-independent, unlike the RGB or HSV color spaces. The equations to achieve the discussed color space transformations can be found in the text by Plataniotis and Venetsanopuolos [37].
Visual Search for Objects in a Complex Visual Context: What We Wish to See
Published in Spyrou Evaggelos, Iakovidis Dimitris, Mylonas Phivos, Semantic Multimedia Analysis and Processing, 2017
Hugo Boujut, Aurèlie Bugeau, Jenny Benois-Pineau
Global image features are generally based on color cues. Indeed, color is an important part of the human visual perception. In images, the colors are encoded in color spaces. A color space is a mathematical model that enables the representation of colors, usually as a tuple of color components. There exist several models of this type, some motivated by the application background, some by the perceptual background of the human visual system. Among them we can cite the RGB (Red Green Blue) space, the HSV (Hue Saturation Value) or the luminance-chrominance spaces (YUV for instance).
Adaptive-sized residual fusion network-based segmentation of biomedical images
Published in Engineering Optimization, 2023
Many different colour representations are used in colour picture processing. Red, green and blue represent colours in the most common RGB space, an orthogonal Cartesian space. According to the tristimulus hypothesis of colour, the human visual system receives colour images from three band passers (three distinct types of photoreceptors in the retina, known as cones), the spectral responses of which are tuned to the wavelengths of red, green and blue. On the other hand, the RGB colour space needs to be adapted to mimic the higher level processes that allow the human visual system to identify a colour. As a result, colour is best characterized in terms of hue, saturation and intensity. A good illustration of this kind of visualization is the HSI space. It can be created from RGB coordinates in a variety of ways, including by determining the hue (H) = arctan p 3(G B); (2R G B), saturation (S) = 1 min (R G B) = I, and intensity (I) = (R G B) = 3, and then entering these values in a cylindrical coordinate system. Similarly to the HSI space, the HSV colour space describes a colour by designating its hue (H), saturation (S) and value (V) = max (R; G; B).
Color-space analytics for damage detection in 3D point clouds
Published in Structure and Infrastructure Engineering, 2022
Mozhgan Momtaz Dargahi, Ali Khaloo, David Lattanzi
A color space is a mathematical model that represents color information as three or four different color components. Different color spaces are used for applications such as computer graphics, image processing, TV broadcasting, and computer vision (Kolkur, Kalbande, Shimpi, Bapat, & Jatakia, 2017). The RGB color space is widely used and is normally the default color space for storing and representing digital images. Other color spaces are often described as a linear or non-linear transformation from RGB (Patil & Patil, 2012). The high correlation between the color components and mixture of chrominance and luminance make RGB difficult for color-based damage detection algorithms(Ibraheem, Hasan, Khan, & Mishra, 2012; Rasras, El, & Skopin, 2007). Appropriate color space selection is fundamental to developing an effective color-based defect detection algorithm (Khan et al., 2012). The choice of appropriate color space is often determined by the performance of the algorithm in minimizing the effects of illumination and viewpoint variations between point clouds and maximizing the separability of defects from the component surface. In this study, five color spaces were evaluated in order to determine the most reliable color representation for damage detection: YIQ, normalized RGB, XYZ, HSI, and L*a*b (Szeliski, 2010; Westland, Ripamonti, & Cheung, 2012; Sundararajan, 2017)
An image authentication technology based on depth residual network
Published in Systems Science & Control Engineering, 2018
Jiafa Mao, Danhong Zhong, Yahong Hu, Weiguo Sheng, Gang Xiao, Zhiguo Qu
The colour space contains many types, such as RGB, HSV, YCbCr, CMY, etc. (Wang et al., 2012). By default, the general image use RGB colour space, but this colour space is not suitable for rendering to human vision. Although HSV describes the colour, from the human visual system in the tone, saturation, value, it is very conducive to image processing and identification. Hue refers to the different colours, saturation refers to a variety of colours, and the value is the degree of light and darkness. The conversion to RGB to HSV space can be done with the following formula: In (8), , R, G, B represent the different colour components of the image pixels, H, S, V correspond to the conversion of the image saturation and the response value of brightness components.