JACIII Vol.14 No.2 pp. 160-166
doi: 10.20965/jaciii.2010.p0160


Fuzzy-Clustering-Based Discriminant Method of Multiple Quadric Surfaces for Noisy and Sparse Range Data

Hideaki Kawano, Hiroshi Maeda, and Norikazu Ikoma

Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kitakyushu 804-8550, Japan

July 9, 2009
October 6, 2009
March 20, 2010
Fuzzy c-Means, Fuzzy c-Varieties, noise clustering, stereo vision, shape modeling
In this paper, a fuzzy-clustering-based discriminant method of multiple quadric surfaces in a scene is proposed. This method is intended for scenes involving multiple objects, where each object is approximated by a primitive model. The proposed method is composed of three steps. In the first step, 3D data is reconstructed using a stereo matching technique from a stereo image whose scene involves multiple objects. Next, the 3D data is divided into a single object by employing Fuzzy c-Means accompanied by Principal Component Analysis (PCA) and a criterion with respect to the number of clusters. Finally, the shape of each object is extracted by Fuzzy c-Varieties with noise clustering. The proposed method was evaluated with respect to some pilot scenes whose ground truth data is known, and it was shown to specify each location and each shape for multiple objects very well.
Cite this article as:
H. Kawano, H. Maeda, and N. Ikoma, “Fuzzy-Clustering-Based Discriminant Method of Multiple Quadric Surfaces for Noisy and Sparse Range Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.2, pp. 160-166, 2010.
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