# Evolving Color Constancy Marc Ebner Universit ä t W ü rzburg, Germany Pattern Recognition Letters 27 （ 2006 ） 1220-1229 Elsevier.

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Evolving Color Constancy Marc Ebner Universit ä t W ü rzburg, Germany Pattern Recognition Letters 27 （ 2006 ） 1220-1229 Elsevier

Algorithms for color constancy  Gamut – constraint methods  Perspective color constancy  Color by correlation  The gray world assumption  Recovery of basis function coefficients  Mechanisms of light adaptation coupled with movements  Neural networks  Comprehensive color normalization  Committee – based methods  Algorithms based on the dichromatic color model  Computation of intrinsic images

PE （ Articial Retina ）  PE ： a rectangular grid of processing elements  Better than neural nets, quite complicated.

Processing elements  1 PE for 1 image pixel  3 layers of PEs carrying out results on the 3 image bands red, green, and blue.  ： Estimate of the illuminant （ color of input pixel ）  The data from other neighboring PEs  Initially, （ ： pixel value ）

Conclusion  Only the current color channel （ band ） is used.  Average data from neighboring elements.

Parallel algorithm  The gray world assumption  The reflectance, ： distributed over the interval [0,1]  From PE, N ： the number of image pixels.

Parallel algorithm  a （ x, y ）： an estimate of local space average color for each image pixel  N （ x, y ）： a set of neighboring elements  （ 1 ） Average the data  （ 2 ） Slowly add the color of the current pixel （ p ： small percentage ）

Parallel algorithm  The two equations, （ 1 ）＆（ 2 ）,are carried out until convergence.  1000, 2000, 3000, 4000, 5000

 Local space average color 1 50 200 1000  The parallel algorithm 1000

Reference  Ebner, M., 2001. Evolving color constancy for an artificial retina. Genetic Programming: Proc. of the 4thEuropean Conference, EuroGP 2001, Lake Como, Italy. Springer-Verlag, Berlin, pp. 11–22.  Ebner, M., 2004. A parallel algorithm for color constancy. J. Parallel Distributed Comput. 64 (1), 79–88.

Why Mondrian has been chosen  First introduced by Edwin Land  No curve and angle. No shade and texture.  Neither uniformly colored nor uniformly bright.  Resemble better the more colorful work of Klee or Lohse.  Anya Hurlbert, 1999

Paul Klee  南方突尼西亞人花園 Tunisian Gardens 1919  Ref. www.writedesignonline.com/history- culture/bauhaus.htm

Richard Paul Lohse  Thematic series in 18 colours A, 1982  Squares formed by colour groups 1944/2  Ref. www.lohse.ch/bio_e.html

Mondrian  Piet Mondrian, Composition A, 1923  www.cartage.org.lb/en/themes/Arts/p ainting/20th-century/art- sake/artsake.htm

Typical Mondrian stimuli  Yellowish daylight ； bluish daylight  2 grey papers （ third from the top on the left ）

The experiment of Kraft and Brainard  Look through a window into a box  A grey test surface against the back wall  A Mondrian-like panel  A tube wrapped in tin foil  A cube, pyramid and tube made from grey cardboard

Local surround  Neutral-illuminant ； Orange-red

Spatial Mean  Neutral-illuminant ； pale-red

Maximum Flux  Neutral-illuminant ； yellow-illuminant

Results  Color constancy

Anya Hurlbert, 2007  Unknown why humans need color constancy. Color? Shape?  How is color constancy measured? with difficulty. Mondrians?  How is color constancy achieved? More than one mechanism. Color processing in the brain.  Retinex

Reference  Hurlbert A (1999) Colour vision: is colour constancy real? Current Biology 9:R558 – R561.  Hurlbert, A. (2007). Colour constancy. Current Biology, 17(21), R906-7.  JM Kraft and DH Brainard, Mechanisms of color constancy under nearly natural viewing. Proc Natl Acad Sci USA 96 (1999), pp. 307 – 312.

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