Object representation and recognition in shape spaces |
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Authors: | J. ZhangAuthor Vitae X. ZhangAuthor VitaeH. KrimAuthor Vitae G.G. WalterAuthor Vitae |
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Affiliation: | a Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA b Department of Electrical Engineering, North Carolina State University, Raleigh, NC 27685-7914, USA c Department of Mathematics, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA |
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Abstract: | In this paper, we describe a shape space based approach for invariant object representation and recognition. In this approach, an object and all its similarity transformed versions are identified with a single point in a high-dimensional manifold called the shape space. Object recognition is achieved by measuring the geodesic distance between an observed object and a model in the shape space. This approach produced promising results in 2D object recognition experiments: it is invariant to similarity transformations and is relatively insensitive to noise and occlusion. Potentially, it can also be used for 3D object recognition. |
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Keywords: | Shape space Object recognition Legendre polynomials Statistical shape analysis Invariants |
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