Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2005.12B.4.429

A Simple Stereo Matching Algorithm using PBIL and its Alternative  

Han Kyu-Phil (금오공과대학교 컴퓨터공학부)
Publication Information
The KIPS Transactions:PartB / v.12B, no.4, 2005 , pp. 429-436 More about this Journal
Abstract
A simple stereo matching algorithm using population-based incremental learning(PBIL) is proposed in this paper to decrease the general problem of genetic algorithms, such as memory consumption and inefficiency of search. PBIL is a variation of genetic algorithms using stochastic search and competitive teaming based on a probability vector. The structure of PBIL is simpler than that of other genetic algorithm families, such as serial and parallel ones, due to the use of a probability vector. The PBIL strategy is simplified and adapted for stereo matching circumstances. Thus, gene pool, chromosome crossover, and gene mutation we removed, while the evolution rule, that fitter chromosomes should have higher survival probabilities, is preserved. As a result, memory space is decreased, matching rules are simplified and computation cost is reduced. In addition, a scheme controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities, like a result of coarse-to-fine matchers. Because of this scheme, the proposed algorithm can produce a stable disparity map with a small fixed-size window. Finally, an alterative version of the proposed algorithm without using probability vector is also presented for simpler set-ups.
Keywords
Stereo Matching; Genetic Algorithm; Disparity Smoothness; PBIL;
Citations & Related Records
연도 인용수 순위
  • Reference
1 David B. Fogel, 'Evolutionary Computation: Comments on the History and Current State,' IEEE Transactions on Evolutionary Computation, Vol.1, No.1, pp.3-17, 1997   DOI
2 D. V. Papadimitriou and T. J. Dennis, 'Epipolar Line Estimation and Rectification for Stereo Image Pairs,' IEEE Transactions on Image Processing, Vol.5, No.4, pp.672-676, 1996   DOI   ScienceOn
3 D. Marr and T. Poggio, A Computational Theory of Human Stereo Vision, Proc. Royal Soc. London, Vol.B204, pp.301-328, 1979   DOI
4 Shumeet Baluja, 'Population-based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning,' Technical reports CMU-CS-94-163, Carnegie Mellon Univ., Jun., 1994
5 P. H. Winston, Artificial Intelligence-3rd edition, New York: Addison-Wesley Publishing Co., 1993, pp.505-528
6 John R. Jordan and Alan C. Bovik, 'Using Chromatic Information in Edge-based Stereo Correspondence,' CVGIP: Image Understanding, Vol.54, No.1, pp.98-118, 1991   DOI
7 John (Juyang) Weng, 'Image Matching Using the Windowed Fourier Phase,' International Journal of Computer Vision, Vol.11, No.3, pp.211-236, 1993   DOI
8 Yong-Suk Kim, Jun-Jae Lee, and Yeong-Ho Ha, 'Stereo Matching Algorithm Based on Modified Wavelet Decomposition Process,' Pattern Recognition, Vol.30, pp.929-952, 1997   DOI   ScienceOn
9 Kyu-Phil Han, Kun-Woen Song, Eui-Yoon Chung, Seok-Je Cho, and Yeong-Ho Ha, 'Stereo Matching Using Genetic Algorithm with Adaptive Chromosomes,' Pattern Recognition, Vol.34, No.9, pp.1729-1740, 2001   DOI   ScienceOn
10 W. Eric L. Grimson, 'Computational Experiments with a Feature Based Stereo Algorithm,' IEEE Trans. Patt. Anal. Machine Intell., Vol.7, No.1, pp.17-34, 1985   DOI   ScienceOn
11 D. De Vleeschauwer, 'An Intensity-based, Coarse-to-fine Approach to Reliably Measure Binocular Disparity,' CVGIP: Image Understanding, Vol.57, No.2, pp.204-218, 1993   DOI   ScienceOn
12 Gerard Medioni and Ramakant Nevatia, 'Segment-based Stereo Matching,' Computer Vision, Graphics, and Image Processing, Vol.31, pp.2-18, 1985   DOI
13 M. Pollefeys, L. Van Goal, M. Vergauwen, F. Verbiest, K. Camelis, J. Tops, R. Koch, 'Visual Modeling with a Hand-held Camera,' International Journal of Computer Vision, vol.59, No.3, pp.207-232, 2004   DOI
14 Kyu-Phil Han, Tae-Min Bae, and Yeong-Ho Ha, 'Hybrid Stereo Matching with a New Relaxation Scheme of Preserving Disparity Discontinuity,' Pattern Recognition, Vol.33, No.5, pp.767-785, 2000   DOI   ScienceOn
15 J. R. Ohm, K. Griineberg, E. Hendriks, M. E. Izquierdo, D. Kalivas, M. Karl, D. Papadimatos, and A. Redert, 'A Realtime Hardware System for Stereoscopic Videoconferencing with Viewpoint Adaptation,' Signal Processing: Image Communication, vol.14, pp.147-171, 1998   DOI   ScienceOn
16 Robert M. Haralick and Linda G. Shapiro, Computer and Robot Vision, Volume 2, New York : Addison-Wesley Publishing Co., 1993, pp.289-378
17 A. C. Kak, Handbook of Industrial Robotics, New York : John Wiley, 1985, pp.272-319
18 S. Panis, M. Ziegler, and J. P. Cosmas, 'The Use of Stereo and Motion in a Generic Object-based Coder,' Signal Processing: Image Communication, vol.9, pp.221-238, 1997   DOI   ScienceOn
19 M. Billinghurst and H. Kato, 'Collaborative Augmented Reality,' Communications of the ACM, vol.45, No.7, pp.64-70, 2002   DOI