A Variable Window Method for Three-Dimensional Structure Reconstruction in Stereo Vision

삼차원 구조 복원을 위한 스테레오 비전의 가변윈도우법

  • 김경범 (국립충주대학교 기계설계학과)
  • Published : 2003.07.01

Abstract

A critical issue in area-based stereo matching lies in selecting a fixed rectangular window size. Previous stereo methods doesn't deal effectively with occluding boundary due to inevitable window-based problems, and so give inaccurate and noisy matching results in areas with steep disparity variations. In this paper, a variable window approach is presented to estimate accurate, detailed and smooth disparities for three-dimensional structure reconstruction. It makes the smoothing of depth discontinuity reduced by evaluating corresponding correlation values and intensity gradient-based similarity in the three-dimensional disparity space. In addition, it investigates maximum connected match candidate points and then devise the novel arbitrarily shaped variable window representative of a same disparity to treat with disparity variations of various structure shapes. We demonstrate the performance of the proposed variable window method with synthetic images, and show how our results improve on those of closely related techniques for accuracy, robustness, matching density and computing speed.

Keywords

References

  1. Jain, R., Kasturi, R. and Schunck, B. G., Machine Vision, McGraw-Hill, 1995
  2. 조진연, 김기범, '스테레오비젼을 이용한 3 차원 물체 측정 시스템,' 한국정밀공학회추계학술대 회논문집, pp. 224-228, 2001
  3. Hoff, W., Ahuja, N., 'Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 2, pp. 121-136,1989 https://doi.org/10.1109/34.16709
  4. Levine, M. D., O'Handley, D. A. and Yagi, G.. M., 'Computer Determination of Depth Maps,' Computer Graphics and Image Processing, Vol. 2, pp. 131-510, 1973 https://doi.org/10.1016/0146-664X(73)90024-5
  5. Kanade, T., Okutomi, M., 'A Stereo Matching Algorithm with an Adaptive Window:Theory and Experiment,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16, No. 9, pp. 920-932, 1994 https://doi.org/10.1109/34.310690
  6. 김경범, 김낙현, 정성종, '강건추정자와 직선마스크를 이용한 스테레오 정합,' 대한기계학회 논문집(A), 제 24 권, 제 4 호, pp. 991-1000, 2000
  7. Kim, G. B., Chung, S. C, 'A New Area-Based Stereo Algorithm for Measurement of 3D Shapes,' Transactions of NAMRI/SME, Vol. 28, pp. 383-388, 2000
  8. 김경범, 정성종, '가변윈도우의 투영왜곡을 고려한 스테레오 정합 알고리듬,' 대한기계학회 논문집(A), 제 25 권, 제 3 호, pp. 461-469, 2001
  9. Dhond, U. R., Aggarwal, J. K., 'Structure from Stereo: A Review,' IEEE Transactions on Systems, Man, and Cybernetics, Vol. 19, No. 6, pp. 1489-1510, 1989 https://doi.org/10.1109/21.44067
  10. Prazdny, K., 'Detection of Binocular Disparities,' Biological Cybernetics, Vol. 52, No. 2, pp. 93-99, 1985 https://doi.org/10.1007/BF00363999
  11. Rodrigue, J. J., Aggarwal, J. K., 'Stochastic Analysis of Stereo Quantization Error,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 5, pp. 467-470, 1990 https://doi.org/10.1109/34.55106
  12. Cox, I. J., Hingornni, S. L. and Rao, S. B., 'Maximum Likelihood Stereo Algorithm,' Computer Vision and Image Understanding, Vol. 63, No. 3, pp. 542-567, 1996 https://doi.org/10.1006/cviu.1996.0040