A System for Finding Changes in Colour

We acquire a great deal of information about the world about us by perceiving the colour of objects. We can manage without colour, but certain tasks can become rather difficult telling ripe from unripe fruits, or finding green tennis balls lost in grass, for example. Yet colour information is seldom used by the machine vision community, although, given the present state of image understanding technology, it is undesirable to waste cues as to the nature of the world observed. The colour signal received by an imaging device does not itself tell us much about the world. We need to compute from this signal the surface reflectances of the objects that were imaged. To do so, we need to be able to find changes in the colour signal. This requires an understanding of what a noteworthy difference in colour is. We discuss metrics on the colour space, colour constancy, and some models of colour vision. We describe a working colour edge finder, based on an opponent coding scheme, which uses Fleck's edge finder to generate a colour edge map.

[1]  Gunther Wyszecki,et al.  Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd Edition , 2000 .

[2]  O. Faugeras.  Digital color image processing within the framework of a human visual model , 1979 .

[3]  D. Hubel,et al.  Anatomy and physiology of a color system in the primate visual cortex , 1984, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[4]  John J. McCann,et al.  Color vision from rod and long-wave cone interactions: Conditions in which rods contribute to multicolored images , 1977, Vision Research.

[5]  Andrew Blake,et al.  Boundary conditions for lightness computation in Mondrian World , 1985, Comput. Vis. Graph. Image Process..

[6]  G. Wyszecki,et al.  Color Science Concepts and Methods , 1982 .

[7]  Margaret M. Fleck.  Local Rotational Symmetries , 1985 .

[8]  Ronald Bert Ohlander,et al.  Analysis of natural scenes. , 1975 .

[9]  J. D. Mollon,et al.  The oddity of blue , 1977, Nature.

[10]  J. Kender.  Saturation, Heu, And Normalized Color: Calculation, Digitization Effects, and Use. , 1976 .

[11]  R. Nevatia.  A Color Edge Detector and Its Use in Scene Segmentation , 1977 .

[12]  J. Bowmaker,et al.  Visual pigments of rods and cones in a human retina. , 1980, The Journal of physiology.

[13]  M. Brady,et al.  Smoothed Local Symmetries and Their Implementation , 1984 .

[14]  Keith Phillips,et al.  Applications of Vector Fields to Image Processing , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  David R. Williams,et al.  Punctate sensitivity of the blue-sensitive mechanism , 1981, Vision Research.

[16]  E H Land,et al.  COLOR VISION AND THE NATURAL IMAGE PART II. , 1959, Proceedings of the National Academy of Sciences of the United States of America.

[17]  David R. Williams,et al.  Foveal tritanopia , 1981, Vision Research.

[18]  Steven A. Shafer,et al.  Using color to separate reflection components , 1985 .

[19]  H. Barlow.  What causes trichromacy? A theoretical analysis using comb-filtered spectra , 1982, Vision Research.

[20]  James J. Clark.  Authenticating Edges Produced by Zero-Crossing Algorithms , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  John D. Mollon,et al.  A TAXONOMY OF TRITANOPIAS , 1982 .

[22]  J. M. Rubin,et al.  Color Vision: Representing Material Categories , 1984 .

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

[24]  N. Daw.  Color-coded cells in goldfish, cat, and rhesus monkey. , 1972, Investigative ophthalmology.

[25]  Brian A. Wandell,et al.  The Synthesis and Analysis of Color Images , 1992, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  J. Beck.  Surface color perception , 1972 .