Relaxation labeling in stereo image matching



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Pajares Martinsanz, Gonzalo and Cruz García, Jesús Manuel de la and López Orozco, José Antonio (2000) Relaxation labeling in stereo image matching. Pattern Recognition, 33 (1). pp. 53-68. ISSN 0031-3203

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This paper outlines a method for solving the global stereovision matching problem using edge segments as the primitives. A relaxation scheme is the technique commonly used by existing methods to solve this problem. These techniques generally impose the following competing constraints: similarity, smoothness, ordering and uniqueness, and assume a bound on the disparity range. The smoothness constraint is basic in the relaxation process. We have verified that the smoothness and ordering constraints can be violated by objects close to the cameras and that the setting of the disparity limit is a serious problem. This problem also arises when repetitive structures appear in the scene (i.e. complex images), where the existing methods produce a high number of failures. We develop our approach from a relaxation labeling method ([1] W.J. Christmas, J. Kittler, M. Petrou, structural matching in computer vision using probabilistic relaxation, IEEE Trans. Pattern Anal. Mach. Intell. 17(8)(1995) 749-764), which allows us to map the above constraints. The main contribution is made, (1) by applying a learning strategy in the similarity constraint and (2) by introducing specific conditions to overcome the violation of the smoothness constraint and to avoid the serious problem produced by the required fixation of a disparity limit. Consequently, we improve the stereovision matching process. A better performance of the proposed method is illustrated by comparative analysis against some recent global matching methods.

Item Type:Article
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© 1999 Pattern Recognition Society

Uncontrolled Keywords:Probabilistic Relaxation, Neural Networks, Vision, Algorithm, Disparity, Criterion, Features, Search
Subjects:Sciences > Computer science
ID Code:26394
Deposited On:27 Aug 2014 09:13
Last Modified:31 Dec 2020 00:03

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