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Choropleth Mapping
Published in Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard, Thematic Cartography and Geovisualization, 2022
Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard
The various forms of color vision impairment can be represented in the CIE color model by a series of confusion lines or lines along which colors are confused with one another (see Chapter 10 for an introduction to color vision impairment and the CIE color model). For example, Figure 15.6 illustrates confusion lines for protanopes and deuteranopes (two subgroups of dichromats, people who are missing red and green cones, respectively, and cannot distinguish red from green). Colors running along the confusion lines will be confused by these groups, whereas colors running roughly perpendicular to the lines should be distinguishable. Using CIE diagrams like the one shown in Figure 15.6, Judy Olson and Cynthia Brewer (1997) developed sets of confusing and accommodating color schemes. Not surprisingly, Olson and Brewer found that color-impaired readers interpreted accommodating schemes more easily.
Computer-aided Diagnosis (CAD) System for Determining Histological Grading of Astrocytoma Based on Ki67 Counting
Published in Varun Bajaj, G.R. Sinha, Computer-aided Design and Diagnosis Methods for Biomedical Applications, 2021
Fahmi Akmal Dzulkifli, Maryam Ahmad Sharifuddin, Mohd Yusoff Mashor, Hasnan Jaafar
The next process was to convert the color space of the image from a RGB to L*a*b* color space. This color space was selected due to the exact color representation and its device-independent color model. Then, the contrast enhancement technique was applied to the luminosity, “L” channel while the a* and b* channels remained unchanged. In this study, Contrast-Limited Adaptive Histogram Equalization was used to enhance the contrast of the astrocytoma images. The CLAHE technique is a variation of an adaptive histogram equalization which reduces noise amplification by limiting contrast amplification [17]. Instead of using the whole image, this technique was performed on small regions called “tiles.” The contrast of each tiles was enhanced, resulting in the histogram of the output region approximately matching the histogram specified by the desired histogram value [18]. Equation 11.1 demonstrates the central equation for enhancing the image: Ix,y=px,yqx,y*maxluminosity
Image Retrieval
Published in Ling Guan, Yifeng He, Sun-Yuan Kung, Multimedia Image and Video Processing, 2012
Color features are extracted in a certain color space. A color space consists of a color model along with a specific mapping of that model onto an absolute color space. Each pixel of the image can be represented as a point in a three-dimensional (3D) color space. Commonly used color space for image retrieval include RGB, Munsell, CIE L*a*b*, CIE L*u*v*, HSV (hue, saturation, and value) (or HSL (hue, saturation, and lightness), HSB (hue, saturation, and brightness)), and opponent color space. Quite a number of studies have been done on color perception and color spaces. Readers interested in learning more can refer to [52,59,93]. In the following sections, we introduce some commonly used color features: color moment, color histogram, and color correlogram.
Least-squares solutions of the reduced biquaternion matrix equation AX=B and their applications in colour image restoration
Published in Journal of Modern Optics, 2019
In this study, we derived the expressions of the minimum norm least-squares solution, the pure imaginary least-squares solution, and the real least-squares solution for the (RB) matrix equation AX=B by using the form of RB matrices, and the Moore–Penrose generalized inverse. Moreover, we investigated their applications in colour image restoration. We applied the image restoration process to the colour image represented by the RGB model with the help of Algorithm 2. Colour images can be represented in different colour models for different applications. A colour model is an abstract mathematical model describing a way the colours can be represented as tuple (ordered list of elements) of numbers (e.g. red, green, blue) in the RGB colour model and (hue, saturation, intensity) in the HSI model, or four in CMYK (cyan, magenta, yellow and black). If transforming the colour image from the CMYK colour space into the RB algebra, the colour image is presented as . In this case, Algorithm 1 can be used for the colour image restoration process. From Figure 1, we observed that the CPU time cost by Algorithm 1 is shorter according to Algorithm 2.
Simple and rapid determination of cephalexin by digital colorimetry using a laboratory-developed smartphone application
Published in Instrumentation Science & Technology, 2022
Anastasiia V. Tumskaia, Ilya V. Loginov, Roman S. Tumskiy, Irina V. Kosyreva
The method of digital colorimetry is based on quantitative description of the color for the analyzed object. Various color models are used for evaluation of the color: RGB (red, green, blue), HSV (hue, saturation, value), and CMYK (cyan, magenta, yellow, key). Smartphones and other recording devices as data readers for the determination of various substances are now widely applied.[26–28] By using these devices, the accuracy is improved in comparison with naked-eye determination.