#11: Robust stereo analysis

K. Palaniappan, Y. Huang, X. Zhuang, and A. F. Hasler

IEEE Int. Symp. Computer Vision, pgs. 175--181, 1995

stereo, image analysis, remote sensing

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One of the most difficult aspects of developing com- putational algorithms for stereopsis that match the intrinsic capabilities of human vision is the correspon- dence problem; that is locating the same point, if it exists, in multi-viewed time-varying sensor measure- ments. Correspondences have been determined us- ing feature-based or region-based matching algorithms with bottom-up or top-down implementations [3]. The bottom-up or low-level approach for stereo analysis in- cludes: i) extracting feature points or area measures in both views, ii) matching the feature points or area measures under certain geometric, illumination, re- flectance and object constraints, and iii) computing a depth or height map using the disparity values from correspondences using sensor geometry and scanning configuration. Most stereo algorithms invariably pro- duce errors due to noise, low image or feature content, geometric distortion, depth discontinuities, occlusion, illumination and reflectance changes across the scene and between views, transparency effects leading to multiple matches, and instability of the cameras and sensors during image formation. Such model viola- tions are difficult to handle in a comprehensive fash- ion. Robust statistical methods have recently been applied to a variety of computer vision problems in- cluding motion estimation [lo] [ll][S],surface recovery from range data [9], and image segmentation [2]. Ro- bust methods offer a powerful alternative to smooth- ness and regularization constraints to mitigate the ef- fects of model errors. A new multistage adaptive ro- bust (MAR) algorithm combined with a multiresolu- tion coarse-to-fine matching model is developed for ro- bust stereo analysis.