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• W2164371126 abstract "The recognition community has typically avoided bridging the representational gap between traditional, low-level image features and generic models. Instead, the gap has been artificially eliminated by either bringing the image closer to the models using simple scenes containing idealized, textureless objects or by bringing the models closer to the images using 3D CAD model templates or 2D appearance model templates. In this paper, we attempt to bridge the representational gap for the domain of model acquisition. Specifically, we address the problem of automatically acquiring a generic 2D view-based class model from a set of images, each containing an exemplar object belonging to that class. We introduce a novel graph-theoretical formulation of the problem in which we search for the lowest common abstraction among a set of lattices, each representing the space of all possible region groupings in a region adjacency graph representation of an input image. The problem is intractable and we present a shortest path-based approximation algorithm to yield an efficient solution. We demonstrate the approach on real imagery." @default.
• W2164371126 created "2016-06-24" @default.
• W2164371126 creator A5030677077 @default.
• W2164371126 creator A5044181099 @default.
• W2164371126 date "2005-07-01" @default.
• W2164371126 modified "2023-09-24" @default.
• W2164371126 title "Generic model abstraction from examples" @default.
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• W2164371126 doi "https://doi.org/10.1109/tpami.2005.139" @default.
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