Abstract
One problem on binocular disparity estimation is, given a point in one view of a scene, to find the homologous point in another view from an adjacent camera. In this work, we use the discrete cosine transform (DCT) with evolutionary methods as an approach to obtain the disparity map. The disparity estimation process is implemented by generating for each image block sets of DCT coefficients. The job of optimising the DCT coefficients, whose inverse transform gives the disparity map, is carried out by a optimisation technique inspired by natural evolution: the genetic algorithm. Matching is performed in the image domain using an intensity similarity measure.
Chapter PDF
Similar content being viewed by others
Keywords
- Genetic Algorithm
- Discrete Cosine Transform
- Discrete Cosine Transform Coefficient
- Stereo Match
- Stereo Pair
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Aschwanden and W. Guggenbühl, “Experimental results from a comparative study on correlation-type registration algorithms”. In Förstner and Rudwiedel (eds.), Robust Computer Vision, 268–282, Wichmann, 1992.
S. T. Barnard and M. A. Fischler, “Computational Stereo”, ACM Computing Surveys, 14(4), Dec. 1982, pp. 553–572.
L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991.
O. Faugeras, Three-Dimensional Computer Vision: A Geometric Viewpoint, MIT Press, Cambridge, Massachusetts, 1993.
D. E. Goldberg, “Genetic algorithms with sharing for multimodal function optimization”, Proc.of the Second Int. Conf. on Genetic Algorithms, 1987, pp. 41–49.
D. E. Goldberg, Genetic Algorithms in search, optimization and machine learning, Addison-Wesley, 1989.
J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.
A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall International, 1989.
M. R. M. Jenkin and A. D. Jepson, “Recovering Local Surface Structure through Local Phase Difference Methods”, CVGIP, vol. 59, 1994, pp. 72–93.
T. Kanade and M. Okutomi, “A stereo matching algorithm with an adaptive window: theory and experiment”, IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-16 (9), 1994, pp. 920–932.
Z. Michalewicz, Genetic Algorithms + data Structures = Evolution Programs, Springer Verlag, Berlin, 1992.
W. H. Press et al., Numerical Recipes in C, Cambridge University Press, 1994.
T. D. Sanger, “Stereo disparity computation using Gabor filters”, Bio. Cybern., vol. 59, 1988, pp. 405–418.
H. Saito and M. Mori, “Application of genetic algorithms to stereo matching of images”, Pattern Recognition Letters, vol. 16, 1995, pp. 815–821.
S. R. Smoot and L. A. Rowe, “Study of DCT coefficient distributions”, Computer Science Division, University of California at Berkeley, Jan. 1996.
R. Vaillant and L. Gueguen, “Genetic Algorithms applied to Binocular Stereovision”, Lecture Notes in Computer Science, Computer Vision-ECCV '94, vol. 801, pp. 193–198.
G. K. Wallace, “The JPEG Still-Picture Compress Standard”, Communications of the ACM, Apr. 1991, vol. 34, no. 4, pp. 30.
G. Wolberg, Digital image warping, IEEE Computer Society Press, Los Alamitos, CA, 1990. *** DIRECT SUPPORT *** A0008188 00010
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pagliari, C.L., Dennis, T.J. (1997). Evolutionary methods applied to binocular disparity estimation. In: Chin, R., Pong, TC. (eds) Computer Vision — ACCV'98. ACCV 1998. Lecture Notes in Computer Science, vol 1351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63930-6_130
Download citation
DOI: https://doi.org/10.1007/3-540-63930-6_130
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63930-5
Online ISBN: 978-3-540-69669-8
eBook Packages: Springer Book Archive