The Representation and Matching of Categorical Shape

We present a framework for categorical shape recognition. The coarse shape of an object is captured by a multiscale blob decomposition, representing the compact and elongated parts of an object at appropriate scales. These parts, in turn, map to nodes in a directed acyclic graph, in which edges enco...

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Bibliographic Details
Main Authors: Shokoufandeh, Ali, Bretzner, Lars, Macrini, Diego, Demirci, M F, Jonsson, Clas, Dickinson, Sven
Format: Report
Language:English
Subjects:
ALGORITHMS
Cybernetics
DECOMPOSITION
GENERIC OBJECT RECOGNITION
GEOMETRIC FORMS
GRAPH MATCHING
MATCHING
PREPRINT
RECOGNITION
SCALE SPACES
SHAPE
SHAPE CATEGORIZATION
SPECTRAL GRAPH THEORY
Theoretical Mathematics
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Summary:We present a framework for categorical shape recognition. The coarse shape of an object is captured by a multiscale blob decomposition, representing the compact and elongated parts of an object at appropriate scales. These parts, in turn, map to nodes in a directed acyclic graph, in which edges encode both semantic relations (parent/child or sibling) as well as geometric relations. Given two image descriptions each represented as a directed acyclic graph, we draw on spectral graph theory to derive a new algorithm for computing node correspondence in the presence of noise and occlusion. In computing correspondence, the similarity of two nodes is a function of their topological (graph) contexts, their geometric (relational) contexts and their node contents. We demonstrate the approach on the domain of view-based 3-D object recognition. Prepared in cooperation with KTH, Stockholm, Sweden, and with University of Toronto, Ontario, Canada. Sponsored in part by NSERC, NSF, and others.