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Scene matching using F-histogram-based features with possibilistic C-means optimization by Ozy Sjahputera; James M. Keller is a Computer Science article available to read on EtoBox.

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This paper outlines further advances from initial findings previously reported in [O. Sjahputera, J.M. Keller, Possibilistic Cmeans in scene matching, Fourth Internat. Conf. of the European Society for Fuzzy Logic and Technology (EUSFLAT), 2005, pp. 669-675]. We propose a scene matching approach based on spatial relationships among objects in the images to determine if two images acquired under different viewing conditions capture the same scene. This is a difficult problem in computer vision. Our approach produces a mapping of objects from one view to the other, and recovers the viewing transformation parameters. The core of the system relies on capturing spatial relationship information through Force Histograms as affine-invariant image descriptors. Object mapping across images is performed by finding the best correspondence map (FMAP) between force histograms in the two images. The major problem is that the number of potential FMAPs is large, even for modest numbers of scene objects. Hence, search optimization is required. The correct FMAP contains histogram correspondences represented by similar feature vectors. Therefore, dense regions in the feature space are suspected to con

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Author

Ozy Sjahputera; James M. Keller

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0165-0114)

Published

2007

Language

EN

Field

Computer Science (Physical Sciences)