Opponent Color Processing Based on Neural Models

In this paper we present a new opponent color system which imitates some of the known color processing neural cells established by electrophysiological recordings. We describe the benefits of this system to image processing tasks. The opponent color model is embedded in an active vision system to improve the systems fixation and recognition capabilities. This is done by removing illumination effects to some degree and by evaluating the resulting color differences. Experimental results are presented.

[1]  S L Guth,et al.  Model for color vision and light adaptation. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[2]  S. Zeki,et al.  Colour coding in rhesus monkey prestriate cortex. , 1973, Brain research.

[3]  Berthold K. P. Horn.  Robot vision , 1986, MIT electrical engineering and computer science series.

[4]  S. Lee Guth,et al.  Unified model for human color perception and visual adaptation II , 1993, Electronic Imaging.

[5]  E H Land,et al.  An alternative technique for the computation of the designator in the retinex theory of color vision. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[6]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[7]  G. Buchsbaum,et al.  Trichromacy, opponent colours coding and optimum colour information transmission in the retina , 1983, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[8]  E H Land,et al.  Recent advances in retinex theory and some implications for cortical computations: color vision and the natural image. , 1983, Proceedings of the National Academy of Sciences of the United States of America.

[9]  D. Hubel,et al.  Spatial and chromatic interactions in the lateral geniculate body of the rhesus monkey. , 1966, Journal of neurophysiology.

[10]  D. Jameson,et al.  Some quantitative aspects of an opponent-colors theory. II. Brightness, saturation, and hue in normal and dichromatic vision. , 1955, Journal of the Optical Society of America.

[11]  P. Cz..  Handbuch der physiologischen Optik , 1896 .

[12]  Berthold K. P. Horn,et al.  Determining lightness from an image , 1974, Comput. Graph. Image Process..

[13]  Bärbel Mertsching,et al.  Entwicklung eines Gegenfarbenmodells für das Neuronale-Active-Vision-System NAVIS , 1995, DAGM-Symposium.

[14]  D. Jameson,et al.  Some Quantitative Aspects of an Opponent-Colors Theory. I. Chromatic Responses and Spectral Saturation , 1955 .

[15]  E. Land,et al.  Lightness and retinex theory. , 1971, Journal of the Optical Society of America.

[16]  D JAMESON,et al.  Some quantitative aspects of an opponent-colors theory. III. Changes in brightness, saturation, and hue with chromatic adaptation. , 1956, Journal of the Optical Society of America.

[17]  Shiro Usui,et al.  Color opponency as the internal representation acquired by a three-layered neural network model , 1993, IEEE International Conference on Neural Networks.

[18]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[19]  Yoichi Miyake,et al.  Color character recognition method based on human perception , 1993 .

[20]  Thomas Young,et al.  On the theory of light and colours , 1967 .