A bayesian approach to binocular stereopsis

International Journal of Computer Vision

Volumn 19, Issue 3, 1996, Pages 237-260

  Belhumeur, Peter N ^a  

^a Yale University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINOCULAR VISION; DYNAMIC PROGRAMMING; GEOMETRY; IMAGE CODING; MATHEMATICAL MODELS; PROBABILITY; SENSORY PERCEPTION;

BAYESIAN APPROACH; BINOCULAR STEREOPSIS; IMAGE FORMATION;

STEREO VISION;

EID: 0030213120     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF00055146     Document Type: Article

References (46)

1. Anderson, B. 1992. Personal communication. Technical Report. Harvard University.
2. Baker, H. and Binford, T. 1981. Depth from edge and intensity based stereo. In IJCAI, pp. 631-636.
3. Belhumeur, P. 1993. A Bayesian Approach to the Stereo Correspondence Problem. Ph.D. thesis, Harvard University.
4. Belhumeur, P. and Mumford, D. 1992. A Bayesian treatment of the stereo correspondence problem using half-occluded regions. In IEEE Proc. Conf. Computer Vision and Pattern Recognition, pp. 506-512.
5. Besag, J. 1974. Spatial interaction and statistical analysis of lattice systems. J. Roy. Stat. Soc. Lond. B., 36:192-225.
6. Blake, A. and Zisserman, A. 1987. Visual Reconstruction. MIT Press: Cambridge, USA.
7. Bulthoff, H., Fahle, M., and Wegmen, M. 1991. Disparity gradients and depth scaling. Perception 20, pp. 145-153.
8. Cernuschi-Frias, B., Belhumeur, P., and Cooper, D. 1985. Estimating and recognizing parameterized 3-D objects using a moving camera. In Proc. Conf. Computer Vision and Pattern Recognition, pp. 167-171.
9. Cernuschi-Frias, B., Cooper, D., Hung, Y., and Belhumeur, P. 1989. Toward a model-based Bayesian theory for estimating and recognizing parameterized 3-D objects using two or more images taken from different positions. IEEE Trans. Pattern Analysis and Machine Intelligence, pp. 1028-1052.
10. Clark, J. and Yuille, A. 1990, Data Fusion for Sensory Information. Kluwer Academic Press: Boston.
11. Cochran, S. and Medioni, G. 1992. 3-D Surface Description from Binocular Stereo. IEEE Trans. Pattern Analysis and Machine Intelligence, pp. 981-994.
12. Cohen, F., Cooper, D., Silverman, J., and Hinkle, E. 1984. Simple parallel hierarchical relaxation algorithms for segmenting textured images based on noncausal Markovian random field models. In Proc. Int. Conf. on Pattern Recognition, Montreal, Canada, pp. 1104-1107.
13. Cox, I.J., Hingorani, S., Maggs, B.M., and Rao, S.B. 1992. Stereo without disparity gradient smoothing: A Bayesian sensor fusion solution. In D. Hogg and R. Boyle, (eds.) British Machine Vision Conference. Springer-Verlag, pp. 337-346.
14. Cross, G. and Jain, A. 1983. Markov random field texture models. IEEE Trans. Pattern Analysis and Machine Intelligence, 5:25-39.
15. Geiger, D. and Girosi, F., Parallel and deterministic algorithms from MRF's: Surface reconstruction, IEEE Trans. Pattern Analysis and machine Intelligence, 13(5):401-412.
16. Geiger, D., Ladendorf, B., and Yuille, A. 1992. Occlusions in binocular stereo. In Proc. European Conf. on Computer Vision, Santa Margherita Ligure, Italy.
17. Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE Trans. Pattern Analysis and machine Intelligence, 6:721-741.
18. Gennery, D. 1980. Modelling the Environment of an Exploring Vehicle by Means of Stereo Vision, Ph.D. thesis, Stanford University.
19. Grenander, U. 1981. Lectures on Pattern Theory, Springer-Verlag.
20. Grimson, W. 1981. From Images to Surfaces. MIT Press:Cambridge, USA.
21. Harris, J. 1989. The coupled depth/slope approach to surface reconstruction. Technical Report, MIT AI Lab Memo TR-908, MIT, Cambridge.
22. Henderson, R., Miller, W., and Grosch, C. 1979. Automatic stereo recognition of man-made targets. Soc. Photo-Optical Instrumentation Engineers.
23. Intille, S. and Bobick, A. 1994. Disparity-space images and large occlusion stereo. In Proc. European Conf. on Computer Vision, pp. 179-186.
24. Jones, D. and Malik, J. 1992. A computational framework for determining stereo correspondence from a set of linear spatial filters. In Proc. European Conf. on Computer Vision, Santa Margherita Ligure, Italy.
25. Julesz, B. 1971. Foundations of Cyclopean Perception. University of Chicago Press.
26. Kato, Z., Berthod, M., and Zerubia, J. 1993. Multiscale markov random field models for parallel image classification. In Proc. Int. Conf. on Computer Vision, pp. 253-257.
27. Lawson, R. and Gulick, W. 1967. Stereopsis and anomalous contour, Vision Research, 7:271-291.
28. Leonardo da Vinci 1989. Leonardo On Painting. Yale University Press: Kemp, M. (Ed.).
29. Lucas, B. and Kanade, T. 1981. An iterative image registration technique with an application to stereo vision. In Proc. of the 7th International Joint Conference on Artificial Intelligence, pp. 674-679.
30. Marr, D. 1982. Vision. Freeman: San Francisco.
31. Marr, D. and Poggio, T. 1976. Cooperative computation of stereo disparity, Science, 194:283-287.
32. Marr, D. and Poggio, T. 1979. A computational theory of human stereo vision. Proc. R. Soc. London, 204:301-328.
33. Marroquin, J., Mitter, S., and Poggio, T. 1987. Probabilistic solutions of ill-posed problems in computational vision. J. of the Am. Stat. Soc., 82(397):76-89.
34. Matthies, L. 1992. Stereo vision for planetary rovers: Stochastic modeling to near real-time implementation. In Journal of Computer Vision, 8(1):71-91.
35. Mumford, D. and Shah, J. 1985. Boundary detection by minimising functionals. In Proc. Conf. Computer Vision and Pattern Recognition, Vol. 22.
36. Nakayama, K. and Shimojo, S. 1980. Da Vinci stereopsis: Depth and subjective occluding contours from unpaired image points. Vision Research, 30:1811-1825.
37. Ohta, Y. and Kanade, T. 1985. Stereo by intra- and inter-scan line search using dynamic programming. IEEE Trans. Pattern Analysis and Machine Intelligence, 7(2):139-154.
38. Poggio, T., Torre, V., and Koch, C. 1985. Computational vision and regularisation theory. Nature, 317:314-319.
39. Pollard, S., Mayhew, J., and Frisby, J. 1985. PMF: A stereo correspondence algorithm using a disparity gradient. Perception, 14:449-470.
40. Szeliski, R. 1989. A Bayesian Modeling of Uncertainty in Low-level Vision. Kluwer Academic Press: Boston.
41. Wahba, G. 1990. Spline models for observational data. In CBMS-NSF, Regional Conference Series in Applied Mathematics, Philadelphia.
42. Wildes, R. 1991. Direct recovery of three-dimensional scene geometry from binocular stereo disparity. IEEE Trans. Pattern Analysis and Machine Intelligence, pp. 761-774.
43. Yang, Y., Yuille, A., and Lu, J. 1993. Local, global, and multilevel stereo matching. In Proc. Conf. Computer Vision and Pattern Recognition, New York.
44. Yuille, A. 1989. Energy functions for early vision and analog networks. Biological Cybernetics, 61:115-123.
45. Zhang, Z. and Faugeras, O. 1992. 3-D Dynamic Scene Analysis: A Stereo Based Approach. Springer-Verlag: Berlin.
46. Zhang, Z., Deriche, R., Luong, L.T., and Faugeras, O. 1994. A robust approach to image matching: Recovery of the epipolar geometry. In Proc. European Conf. on Computer Vision, pp. 179-186.