Lecture Notes in Computer Science vol:4170 pages:145-169
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Methods based on local, viewpoint invariant features have
proven capable of recognizing objects in spite of viewpoint changes, occlusion and clutter. However, these approaches fail when these factors are too strong, due to the limited repeatability and discriminative power of the features. As additional shortcomings, the objects need to be rigid and
only their approximate location is found. We present an object recognition approach which overcomes these limitations. An initial set of feature correspondences is first generated. The method anchors on it and then gradually explores the surrounding area, trying to construct more and more matching features, increasingly farther from the initial ones. The resulting process covers the object with matches, and simultaneously separates the correct matches from the wrong ones. Hence, recognition and
segmentation are achieved at the same time. Only very few correct initial matches suffice for reliable recognition. Experimental results on still images and television news broadcasts demonstrate the stronger power of the presented method in dealing with extensive clutter, dominant occlu-
sion, large scale and viewpoint changes. Moreover non-rigid deformations are explicitly taken into account, and the approximative contours of the object are produced. The approach can extend any viewpoint invariant feature extractor.
Ferrari V., Tuytelaars T., Van Gool L., ''Simultaneous object recognition and segmentation by image exploration'', Lecture notes in computer science : toward category-level object recognition, vol. 4170, pp. 145-169, 2006.