GPU-based Stereo Matching Algorithm with the Strategy of Population-based Incremental Learning

  • Nie, Dong-Hu ;
  • Han, Kyu-Phil ;
  • Lee, Heng-Suk
  • Published : 2009.06.30


To solve the general problems surrounding the application of genetic algorithms in stereo matching, two measures are proposed. Firstly, the strategy of simplified population-based incremental learning (PBIL) is adopted to reduce the problems with memory consumption and search inefficiency, and a scheme for controlling the distance of neighbors for disparity smoothness is inserted to obtain a wide-area consistency of disparities. In addition, an alternative version of the proposed algorithm, without the use of a probability vector, is also presented for simpler set-ups. Secondly, programmable graphics-hardware (GPU) consists of multiple multi-processors and has a powerful parallelism which can perform operations in parallel at low cost. Therefore, in order to decrease the running time further, a model of the proposed algorithm, which can be run on programmable graphics-hardware (GPU), is presented for the first time. The algorithms are implemented on the CPU as well as on the GPU and are evaluated by experiments. The experimental results show that the proposed algorithm offers better performance than traditional BMA methods with a deliberate relaxation and its modified version in terms of both running speed and stability. The comparison of computation times for the algorithm both on the GPU and the CPU shows that the former has more speed-up than the latter, the bigger the image size is.


Image filtering;Performance Evaluation;General-Purpose Computation Based on GPU;GPU;Population-Based Incremental Learning


  1. D.Luebke, M. Harris, J.Kruger, T. Purcell, N.Govind araju etc.. GPGPU: general purpose computation on graphics hardware, in ACM SIGGRAPH 2004 Course Notes, ACM, New York, NY, 2004, pp.33
  2. P. H. Winston, Artificial Intelligence-3rd edition, New York: Addison-Wesley Publishing Co., pp.505- 528, 1993
  3. Fang-Chih Tien, Te-Hsiu Sun. Solving Line-Feature Stereo Matching with Genetic Algorithms in Hough Space. Journal of the Chinese Institute of Industrial Engineers, Vol.21, No.5, pp.516-526, 2004
  4. Regis Vaillant and Laurent Gueguen. Genetic algorithms applied to binocular stereovision. Computer Vision- ECCV '94. Vol.801, pp.193-198, 2006
  5. 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
  6. Q. Yang, L. Wang, R. yang, H. Stewenius, and D. nister, 'Stereo Matching with Color-Weighted correlation, hierarchical Belief Propagation and occlusion Handling' Proceedings of the 2006 IEEE Computer society Conference on Computer Vision and Pattern recognition (CVPR'6), 2006, pp.2347-2354
  7. John (Juyang) Weng, 'Image Matching Using the Windowed Fourier Phase,' International Journal of Computer Vision, Vol.11, No.3, pp.211-236, 1993
  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
  9. Ondrej Fialka and Martin Cardik. Cadlk, 'FFT and Convolution Performance in Image Filtering on GPU,' Proceedings of the 10th International Conference on Information Visualisation, Los Alamitos, IEEE Computer Society, pp.609-614, 2006
  10. Kenneth Moreland and Edward Angel, 'The FFT on a GPU,' SIGGRAPH/Eurographics Workshop on Graphics Hardware 2003 Proceedings, San Diego, pp.112-119, 2003
  11. J. KRUGER AND R. WESTERMANN, Linear algebra operators for GPU implementation of numerical algorithms, in ACM SIGGRAPH 2005 Courses, ACM, New York, NY, 2005, p.234
  12. J. BOLZ, I. FARMER, E. GRINSPUN, AND P. SCHRO ODER, Sparse matrix solvers on the GPU: conjugate gradients and multigrid, in ACM SIGGRAPH 2003 Papers, ACM, New York, NY, 2003, pp.917. 924
  13. 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
  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
  15. Kyu-Phil Han, 'A Simple Stereo Matching Algorithm Using PBIL and its Alternative' Korea Information Processing Society, Vol.12-B, No.4, pp.429-436, Aug., 2005
  16. 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
  17. 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
  18. M. Pollefeys, L. Van Gool, M. Vergauwen, F. Verbiest, K. Cornelis, J. Tops, R. Koch, 'Visual Modeling with a Hand-held Camera,' International Journal of Computer Vision, Vol.59, No.3, pp.207-232, 2004
  19. D. Marr and T. Poggio, A Computational Theory of Human Stereo Vision, Proc. Royal Soc. London, Vol. B204, pp.301-328, 1979

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