2002 | OriginalPaper | Buchkapitel
Class-Specific, Top-Down Segmentation
verfasst von : Eran Borenstein, Shimon Ullman
Erschienen in: Computer Vision — ECCV 2002
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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In this paper we present a novel class-based segmentation method, which is guided by a stored representation of the shape of objects within a general class (such as horse images). The approach is different from bottom-up segmentation methods that primarily use the continuity of grey-level, texture, and bounding contours. We show that the method leads to markedly improved segmentation results and can deal with significant variation in shape and varying backgrounds. We discuss the relative merits of class-specific and general image-based segmentation methods and suggest how they can be usefully combined.