Hybrid stereo matching with a new relaxation scheme of preserving disparity discontinuity

Pattern Recognition

Volumn 33, Issue 5, 2000, Pages 767-785

  Han, Kyu Phil ^a   Bae, Tae Min ^a   Ha, Yeong Ho ^a  

^a Kyungpook National University   (South Korea)

Author keywords

Adaptive window; Disparity discontinuity preservation; Edge and region based matching; Excitatory and inhibitory inputs; Relaxation; Smoothness and uniqueness constraints; Stereo matching

Indexed keywords

ADAPTIVE FILTERING; ALGORITHMS; BINOCULAR VISION; CONSTRAINT THEORY; EDGE DETECTION; IMAGE PROCESSING; NONLINEAR FILTERING; STEREO VISION;

ADAPTIVE WINDOW; DISPARITY DISCONTINUITY PRESERVATION; STEREO MATCHING;

COMPUTER VISION;

EID: 0033882606     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(99)00095-3     Document Type: Article

References (24)

1. Kak A.C. Handbook of Industrial Robotics, Chapter on Depth Perception for Robots. 1985;Wiley, New York.
2. G. Stockman, S. Chen, G. Hu, N. Shrikhande, Recognition of rigid objects using structured light, in Proceedings of 1987 IEEE International Conference on Systems Man and Cybernetics 1987, pp. 877-883.
3. R.M. Haralick, L.G. Shapiro, Computer and Robot Vision, part 2, 1992, (Chapter 16).
4. Eric W., Grimson L. Computational experiments with a feature based stereo algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 7(1):1985;17-34.
5. Medioni G., Nevatia R. Segment-based stereo matching, Compu. Vision Graphics Image Process. 31:1985;2-18.
6. Song De Ma Conics-based stereo, motion estimation, and pose determination. Int. J. Comput. Vision. 10(1):1993;7-25.
7. Marr D., Poggio T. Cooperative computation of stereo disparity. Science. 194:1976;283-287.
8. J.P. Frisby, S.B. Pollard, Computational Issues in solving the stereo correspondence problem, Computational Models of Visual Processing, part 7, 1990, pp. 331-357 (Chapter 22).
9. Marr D., Poggio T. A computational theory of human stereo vision. Proc. Roy. Soc. London. B204:1979;301-328.
10. De Vleeschauwer D. An intensity-based, coarse-to-fine approach to reliably measure binocular disparity. CVGIP. Image Understanding 57(2):1993;204-218.
11. Jun-Jae Lee, Jae-Chang Shim, Yeong-Ho Ha Stereo correspondence using hopfield neural network of new energy function. Pattern Recognition. 27:1994;1513-1522.
12. Yong-Suk Kim, Jun-Jae Lee, Yeong-Ho Ha Stereo matching algorithm based on modified wavelet decomposition process. Pattern Recognition. 30:1997;929-952.
13. John (Juyang) Weng, Image matching using the windowed fourier phase, Int. J. Comput. Vision 11 (3) (1993) 211-236.
14. Marapane S.B., Trived M.M. Region-based stereo analysis for robotic applications. IEEE Trans. Systems Man Cybernet. 19:1989;1447-1464.
15. Jordan J.R., Bovik A.C. Using chromatic information in edge-based stereo correspondence. CVGIP. Image Understanding 54(1):1991;98-118.
16. Knotanzad A., Bokil A., Lee Y.W. Stereopsis by constraint learning feed-forward neural networks. IEEE Trans. Neural Networks. 4:1993;332-342.
17. Okutomi M., Kanade T. A multiple-basedline stereo. IEEE Trans. Pattern. Anal. Mach. Intell. 15(4):1993;353-363.
18. Van Vliet L.J., Young I.T. A nonlinear laplace operator as edge detector in noisy images. Comput. Vision Graphics Image Process. 45:1989;167-195.
19. Burt P.J. Fast filter transforms for image processing. Comput. Graphics Image Process. 16:1981;20-51.
20. Okutomi M., Kanade T. A locally adaptive window for signal matching. Int. J. Comput. Vision. 7(2):1992;143-162.
21. Kyeong-Hoon Do, Yong-Suk Kim, Tae-Uk Uam, Yeong-Ho Ha Iterative relaxational stereo matching based on adaptive support between disparities. Pattern Recognition. 31(8):1998;1049-1059.
22. Shing-Huan Lee, Jin-Jang Leou A dynamic programming approach to line segment matching in stereo vision. Pattern Recognition. 27(8):1994;961-986.
23. Torre V., Poggio T. On edge detection. IEEE Trans. Pattern. Anal. Mach. Intell. PAMI-8:1986;147-163.
24. Haralick R.M., Shapiro L.G. Computell and Robot Vision, part 1. 1992;37-48.