Vision, computer

J. L. C. Sanz, E. B. Hinkle, and A. K. Jain, Raidon and Projection Transform-Based Computer Vision, Springer-Vedag, Berlin, 1988. 

Computers are well suited to the manipulation of numbers, but the ES relies on symbolic computation, in which symbols stand for properties, concepts, and relationships. The degree to which an ES can manage a task may depend on the complexity of the problem. For example, computer vision is an area of great interest within AI and many programs exist that can, without human assistance, use the output from a digital camera to extract information, such as the characters on a car number plate. However, automatic analysis of more complex images, such as a sample of soil viewed through a microscope, is far... 

Essa, I. Pentland, A. 1995. Facial expression recognition using a dynamic model and motion energy. Int l Conference on Computer Vision, Cambridge, MA, June 20-23, 1995. 

Kakadiaris, I. A., Metaxas, D. Bajcsy, R. 1994. Active part-decomposition, shape and motioon estimation of articulated objects A physics-based approach. Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 980-984. Seattle, Washington. 

Proceedings of the IEEE International Conference on Computer Vision. 1988, 238-249. 

Sandak, B., Nussinov, R., and Woleson, H.J. An automated computer vision and robotics-based technique for 3D flexible biomolecular docking and matching. Comput. Appl. Biol. Sci. 1995, 11, 87-99. 

Fish Quality Control by Computer Vision, edited by L. F. Pau and R. Olafsson... 

Color processing performs a very important role in computer vision. Many tasks become much simpler if the accurate color of objects is known. Accurate color measurement is required for color-based object recognition. Many objects can be distinguished on the basis of their color. Suppose that we have a yellow book and a red folder. We can distinguish the two easily because one is yellow and the other is red. But color can also be used in other areas of computer vision such as the computation of optical flow or depth from stereo based on color and shading. In this book, we will have an in-depth look at color perception and color processing. 

Figure 8.9 Performance of best evolved individual on three different fitness cases. The images in the first row show the reflectance images. The images in the second row show the virtual illuminant. The images in the third row show the input images presented to the individual. The images in the fourth row show the illuminant that was estimated by the evolved individual. In the last row, the estimated reflectance is shown. (Reproduced from Ebner M. 2006 Evolving color constancy. Special issue on evolutionary computer vision and image understanding of pattern recognition letters, Elsevier, 27(11), 1220-1229, by permission from Elsevier.)...

Figure D.l Image set 1 (lambertian objects). (Original image data from Data for Computer Vision and Computational Colour Science made available through http //www.cs.sfu.ca/ colour/data/index.html. See Barnard K, Martin L, Funt B and Coath A 2002 A data set for color research, Color Research and Application, Wiley Periodicals, 27(3), 147-151. Reproduced by permission of Kobus Barnard.)...

Barnard K, Finlayson G and Funt B 1997 Color constancy for scenes with varying illumination. Computer Vision and Image Understanding 65(2), 311-321. 

Barnard K, Martin L and Funt B 2000 Colour by correlation in a three dimensional colour space In Proceedings of the 6th European Conference on Computer Vision, Dublin, Ireland (ed. Vernon D), pp. 375-389. Springer-Verlag, Berlin. 

Berwick D and Lee SW 1998 A chromaticity space for specularity, illumination color- and illumination pose-invariant 3-d object recognition Sixth International Conference on Computer Vision. Narosa Publishing, pp. 165-170. 

Blake A 1985 Boundary conditions for lightness computation in mondrian world. Computer Vision, Graphics, and Image Processing 32, 314-327. 

Drew MS, Finlayson GD and Hordley SD 2003 Recovery of chromaticity image free from shadows via illumination invariance ICCV 03 Workshop on Color and Photometric Methods in Computer Vision. Nice, France, pp. 32-39. 

Ebner M 2004b Color constancy using local color shifts In Proceedings of the 8th European Conference on Computer Vision, Part III, Prague, Czech Republic, May, 2004 (eds. Pajdla T and Matas J), pp. 276-287. Springer-Verlag, Berlin. 

Ebner M 2006 Evolving color constancy. Special Issue on Evolutionary Computer Vision and Image Understanding of Pattern Recognition Letters 27(11), 1220-1229. 

Finlayson GD and Drew MS 2001 4-sensor camera calibration for image representation invariant to shading, shadows, lighting, and specularities. Proceedings of the 8th IEEE International Conference on Computer Vision, Volume 2, Vancouver, Canada, July 9-12, 2001, pp. 473-480. 

Finlayson GD and Schaefer G 2001 Solving for colour constancy using a constrained dichromatic reflection model. International Journal of Computer Vision 42(3), 127-144. 

Finlayson GD, Schiele B and Crowley JL 1998 Comprehensive colour image normalization In Fifth European Conference on Computer Vision (ECCV 98), Freiburg, Germany (eds. Burkhardt H and Neumann B), pp. 475-490. Springer-Verlag, Berlin. 

Forsyth DA 1988 A novel approach to colour constancy Second International Conference on Computer Vision (Tampa, FL, Dec. 5-8). IEEE Press, pp. 9-18. 

Funt BV and Drew MS 1988 Color constancy computation in near-mondrian scenes using a finite dimensional linear model In Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition, Ann Arbor, MI (eds. Jain R and Davis L), pp. 544-549. Computer Society Press. 

Funt BV, Drew MS and Ho J 1991 Color constancy from mutual reflection. International Journal of Computer Vision 6(1), 5-24. 

Parker JR 1997 Algorithms for Image Processing and Computer Vision. John Wiley Sons, New York. 

Schiele B and Crowley JL 1996 Object recognition using multidimensional receptive field histograms In Fourth European Conference On Computer Vision, Cambridge, UK, April 14-18 (eds. Buxton B and Cipolla R), pp. 610-619. Springer-Verlag, Berlin. 

Schiele B and Crowley JL 2000 Recognition without correspondence using multidimensional receptive field histograms. International Journal of Computer Vision 36(1), 31-52. 

Shapiro LG and Stockman GC 2001 Computer Vision. Prentice Hall, New Jersey. 

Swain MJ and Ballard DH 1991 Color indexing. International Journal of Computer Vision 7, 11-32. 

Weickert J 1997 A review of nonlinear diffusion filtering In Scale-Space Theory in Computer Vision (eds. ter Haar Romeny B, Florack L, Koenderink J and Viergever M), pp. 3-28. Springer-Verlag, Berlin. 

Original image data from Data for Computer Vision and... 

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