SCALE-SPACE FRAMEWORK FOR EFFICIENT SEGMENTATION OF POINT-SAMPLED GEOMETRY(International Workshop on Advanced Image Technology 2005)
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概要
- 論文の詳細を見る
In this paper, we introduce a novel framework for analyzing and segmenting point-sampled 3D objects. Our algorithm computes a decomposition of a given point set surface into meaningful components, which are delimited by line features and deep concavities (points of high negative curvature). Central to our method is the combination of the scale-space theory, line feature extraction and convexity measure to compute a new surface classifier which is then used in an anisotropic diffusion process via partial differential equations (PDEs). The algorithm avoids the misclassifications due to fuzzy and soft line features. It operates directly on points requiring no vertex connectivity information. We demonstrate and discuss its performance on a collection of point sampled 3D objects including CAD and natural models.
- 2005-01-04
著者
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TAKAHASHI Hiroki
Graduate School of Information Science, Nara Institute of Science and Technology
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NAKAJIMA Masayuki
Graduate School of Information Science & Engineering, Tokyo Institute of Technology
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Laga Hamid
Graduate School of Information Science and Engineering Tokyo Institute of Technology
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Takahashi H
Department Of Human Communication The University Of Electro-communications
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Takahashi H
Tokyo Inst. Technol. Jpn
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Laga Hamid
Global Edge Institute Tokyo Institute Of Technology
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Takahashi H
Tokyo Inst. Technol. Tokyo Jpn
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Nakajima Masayuki
Graduate School Of Information And Engineering Tokyo Institute Of Technology
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Takahashi Hiroki
Graduate School Of Human And Environmental Studies Kyoto University
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Takahashi Hiroki
Graduate School of Biomedical Engineering, Tohoku University, Sendai 980-8579, Japan
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