Image Color Mapping and Clustering in Luma/Chroma Fundamental Color Space
This paper presents a Luma/Chroma opponent-color space that is created from spectral decomposition of fundamental based on matrix R theory. A new color space has orthogonal opponent-color axes with hue linearity because it's created through a linear naive transformation of fundamental in FCS.
The key points lie in that the “chromatic” projector RC is further decomposed into RR and RB opponent-color components and an orthogonal Luma/Chroma FCS is newly created by a set of (RA , RR , RB ), each composed of n×n matrix. Now image colors are mapped onto Luma/Chroma FCS. First, a tristimulus value XYZ from sRGB camera input is transformed back to the fundamental C* by pseudo-inverse projection. Next, C* is decomposed into the spectral triplet (CA *, CR *, CB *) through the (RA , RR , RB ). Finally, the achromatic fundamental CA *(λ), n-dimensional vector, is converted to the luminance value LA by integral over λ. As well, the chromatic fundamentals, CR *(λ) and CB *(λ) are converted to the chrominance values CR and CB . The paper shows how the image colors are mapped onto (LA , CR , CB ) Luma/Chroma space and introduces its application to the image segmentation in comparison with conventional CIELAB and IPT color spaces.
Document Type: Research Article
Publication date: January 1, 2008
Started in 2002 and merged with the Color and Imaging Conference (CIC) in 2014, CGIV covered a wide range of topics related to colour and visual information, including color science, computational color, color in computer graphics, color reproduction, volor vision/psychophysics, color image quality, color image processing, and multispectral color science. Drawing papers from researchers, scientists, and engineers worldwide, DGIV offered attendees a unique experience to share with colleagues in industry and academic, and on national and international standards committees. Held every year in Europe, DGIV papers were more academic in their focus and had high student participation rates.
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