15 March 1996 Stereo disparity computation using Gabor filters and feature selection techniques
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This paper deals with the recovery of a scene from a pair of images, where each image is acquired from a different viewpoint. The central problem is the identification of corresponding points in all views. Basically two approaches have evolved: area-based methods, which employ local graylevel correlation techniques; and feature-based methods, which use preprocessing steps to extract local feature vectors and match these entities. Previous work has shown that feature-based methods have advantages both in terms of computational complexity, and accuracy. We extend these comparative studies, which had compared both philosophies, to a new type of feature extraction technique. This technique handles the correspondence problem by matching two sets of dense feature vectors, generated by GABOR filters. Gabor filters have been used previously for recognition of blob-type targets and texture classification. We show how the two techniques can be used as two independent sources to derive feature- vectors. Consequently, fusion of the two sources improves the accuracy of correspondence detection.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wolfgang Poelzleitner, Wolfgang Poelzleitner, David P. Casasent, David P. Casasent, } "Stereo disparity computation using Gabor filters and feature selection techniques", Proc. SPIE 2752, Optical Pattern Recognition VII, (15 March 1996); doi: 10.1117/12.235660; https://doi.org/10.1117/12.235660

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