Model-based interpretation of stereo imagery of textured surfaces

Abstract. We present a scheme for reliable and accurate surface reconstruction from stereoscopic images containing only fine texture and no stable high-level features. Partial shape information is used to improve surface computation: first by fitting an approximate, global, parametric model, and then by refining this model via local correspondence processes. This scheme eliminates the window size selection problem in existing area-based stereo correspondence schemes. These ideas are integrated in a practical vision system that is being used by environmental scientists to study wind erosion of bulk material such as coal ore being transported in open rail cars.

[1]  Nagaraj Nandhakumar,et al.  An automated stereoscopic coal profiling system-CCLPS , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[2]  Eric L. W. Grimson,et al.  From Images to Surfaces: A Computational Study of the Human Early Visual System , 1981 .

[3]  Nagaraj Nandhakumar,et al.  An accurate stereo correspondence method for textured scenes using improved power cepstrum techniques , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Demetri Terzopoulos,et al.  The Computation of Visible-Surface Representations , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Thomas J. Olson,et al.  Stereopsis for verging systems , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Takeo Kanade,et al.  A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Thomas S. Huang,et al.  Learning and Feature Selection in Stereo Matching , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Laurent Vinet,et al.  Hierarchical region based stereo matching , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Narendra Ahuja,et al.  Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Jake K. Aggarwal,et al.  Structure from stereo-a review , 1989, IEEE Trans. Syst. Man Cybern..

[11]  Wenyi Zhao,et al.  Effects of camera alignment errors on stereoscopic depth estimates , 1996, Pattern Recognit..

[12]  H. K. Nishihara,et al.  Practical Real-Time Imaging Stereo Matcher , 1984 .

[13]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.

[14]  Edward H. Adelson,et al.  Single Lens Stereo with a Plenoptic Camera , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  David B. Cooper,et al.  Computing correspondence based on regions and invariants without feature extraction and segmentation , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[16]  J. C. Hassab,et al.  Analysis of signal extraction, echo detection and removal by complex cepstrum in presence of distortion and noise , 1975 .

[17]  Mohan M. Trivedi,et al.  Multi-Primitive Hierarchical (MPH) Stereo Analysis , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  William B. Thompson,et al.  Disparity Analysis of Images , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  David Marr,et al.  Visual information processing: artificial intelligence and the sensorium of sight , 1987 .

[20]  Ramakant Nevatia,et al.  Segment-based stereo matching , 1985, Comput. Vis. Graph. Image Process..

[21]  Yehezkel Yeshurun,et al.  Cepstral Filtering on a Columnar Image Architecture: A Fast Algorithm for Binocular Stereo Segmentation , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Martin A. Fischler,et al.  Computational Stereo , 1982, CSUR.

[23]  James J. Little,et al.  Visual echo analysis , 1993, 1993 (4th) International Conference on Computer Vision.

[24]  Thomas O. Binford,et al.  Depth from Edge and Intensity Based Stereo , 1981, IJCAI.

[25]  A. W. M. van den Enden,et al.  Discrete Time Signal Processing , 1989 .