Color space distortions in patients with type 2 diabetes mellitus

Color vision impairment was examined in patients with type 2 diabetes mellitus (DM2) without retinopathy. We assessed the type and degree of distortions of individual color spaces. DM2 patients (n = 32), and age-matched controls (n = 20)were tested using the Farnsworth D-15 and the Lanthony D-15d tests. In addition, subsets of caps from both tests were employed in a triadic procedure (Bimler & Kirkland, 2004). Matrices of inter-cap subjective dissimilarities were estimated from each subject’s “odd-one-out” choices, and processed using non-metric multidimensional scaling. Two-dimensional color spaces, individual and group (DM2 patients; controls), were reconstructed, with the axes interpreted as the R0G and B0Y perceptual opponent systems. Compared to controls, patient results were not significant for the D-15 and D-15d. In contrast, in the triadic procedure the residual distances were significantly different compared to controls: right eye, P 0.021, and left eye, P 0.022. Color space configurations for the DM2 patients were compressed along the B0Y and R0G dimensions. The present findings agree with earlier studies demonstrating diffuse losses in early stages of DM2. The proposed method of testing uses color spaces to represent discrimination and provides more differentiated quantitative diagnosis, which may be interpreted as the perceptual color system affected. In addition, it enables the detection of very mild color vision impairment that is not captured by the D-15d test. Along with fundoscopy, individual color spaces may serve for monitoring early functional changes and thereby to support a treatment strategy

Color Space Selection for Self-Organizing Map Based Foreground Detection in Video Sequences

The selection of the best color space is a fundamental task in detecting foreground objects on scenes. In many situations, especially on dynamic backgrounds, neither grayscale nor RGB color spaces represent the best solution to detect foreground objects. Other standard color spaces, such as YCbCr or HSV, have been proposed for background modeling in the literature; although the best results have been achieved using diverse color spaces according to the application, scene, algorithm, etc. In this work, a color space and color component weighting selection process is proposed to detect foreground objects in video sequences using self-organizing maps. Experimental results are also provided using well known benchmark videos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Modelling, Measuring and Compensating Color Weak Vision

We use methods from Riemann geometry to investigate transformations between the color spaces of color-normal and color weak observers. The two main applications are the simulation of the perception of a color weak observer for a color normal observer and the compensation of color images in a way that a color weak observer has approximately the same perception as a color normal observer. The metrics in the color spaces of interest are characterized with the help of ellipsoids defined by the just-noticable-differences between color which are measured with the help of color-matching experiments. The constructed mappings are isometries of Riemann spaces that preserve the perceived color-differences for both observers. Among the two approaches to build such an isometry, we introduce normal coordinates in Riemann spaces as a tool to construct a global color-weak compensation map. Compared to previously used methods this method is free from approximation errors due to local linearizations and it avoids the problem of shifting locations of the origin of the local coordinate system. We analyse the variations of the Riemann metrics for different observers obtained from new color matching experiments and describe three variations of the basic method. The performance of the methods is evaluated with the help of semantic differential (SD) tests.Comment: Full resolution color pictures are available from the author

Contrast enhacenment of RGB color images by histogram equalization of color vectors' intensities

Mejora del contraste de imagenes de color RGBThe histogram equalization (HE) is a technique developed for image contrast enhancement of grayscale images. For RGB (Red, Green, Blue) color images, the HE is usually applied in the color channels separately; due to correlation between the color channels, the chromaticity of colors is modified. In order to overcome this problem, the colors of the image are mapped to different color spaces where the chromaticity and the intensity of colors are decoupled; then, the HE is applied in the intensity channel. Mapping colors between different color spaces may involve a huge computational load, because the mathematical operations are not linear. In this paper we present a proposal for contrast enhancement of RGB color images, without mapping the colors to different color spaces, where the HE is applied to the intensities of the color vectors. We show that the images obtained with our proposal are very similar to the images processed in the HSV (Hue, Saturation, Value) and L*a*b* color spaces