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

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Presentation on theme: "Evolving Color Constancy Marc Ebner Universit ä t W ü rzburg, Germany Pattern Recognition Letters 27 ( 2006 ) 1220-1229 Elsevier."— Presentation transcript:

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

2 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

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

4 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 )

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

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

8 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 )

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

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11  Local space average color  The parallel algorithm 1000

12 Reference  Ebner, M., 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., A parallel algorithm for color constancy. J. Parallel Distributed Comput. 64 (1), 79–88.

13 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

14 Paul Klee  南方突尼西亞人花園 Tunisian Gardens 1919  Ref. culture/bauhaus.htm

15 Richard Paul Lohse  Thematic series in 18 colours A, 1982  Squares formed by colour groups 1944/2  Ref.

16 Mondrian  Piet Mondrian, Composition A, 1923  ainting/20th-century/art- sake/artsake.htm

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

18 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

19 Local surround  Neutral-illuminant ; Orange-red

20 Spatial Mean  Neutral-illuminant ; pale-red

21 Maximum Flux  Neutral-illuminant ; yellow-illuminant

22 Results  Color constancy

23 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

24 Reference  Hurlbert A (1999) Colour vision: is colour constancy real? Current Biology 9:R558 – R561.  Hurlbert, A. (2007). Colour constancy. Current Biology, 17(21), R  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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