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

Paper:

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

Received:
July 9, 2009
Accepted:
October 6, 2009
Published:
March 20, 2010
Keywords:
Fuzzy c-Means, Fuzzy c-Varieties, noise clustering, stereo vision, shape modeling
Abstract

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:
Hideaki Kawano, Hiroshi Maeda, and Norikazu 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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References
  1. [1] G. O. Young, “Synthetic structure of industrial plastics (Book style with paper title and editor),” in Plastics, 2nd ed., Vol.3, J. Peters, Ed., New York: McGraw-Hill, pp. 15-64, 1964.
  2. [2] J. Williams and M. Bennamoun, “A Multiple view 3D registration algorithm with statistical error modeling,” IEICE Trans. Inf. & Syst., Vol.E83-D, No.8, pp. 1662-1670, 2000.
  3. [3] C. Wang, H. Takahashi, H. Hirayu, Y. Niwa, and K. Yamamoto, “Polyhedral description of panoramic range data by stable plane extraction,” IEICE Trans. Inf. & Syst., Vol.E85-D, No.9, pp. 1399-1408, 2002.
  4. [4] G. K. Kraetzschmar and S. Enderle, “Self-localization using sporadic features,” Robotics and Autonomous Systems, Vol.40, Issues 2-3, pp. 111-119, 2002.
  5. [5] D. Hahnel, W. Burgard, and S. Thrun, “Learning compact 3D models of indoor and outdoor environments with a mobile robot,” Robotics and Autonomous Systems, Vol.44, pp. 15-27, 2003.
  6. [6] Y. Y. Cha and D. G. Gweon, “A calibration and range-data extraction algorithm for an omnidirectional laser range finder of a freeranging mobile robot,” Mechatronics, Vol.6, pp. 665-689, 1996.
  7. [7] A. A. Y. Mustafa, L. G. Shapiro, and M. A. Ganter, “3D object identification with color and curvature signatures,” Pattern Recognition, Vol.32, pp. 339-355, 1999.
  8. [8] H. Dijck and F. Heijden, “Object recognition with stereo vision and geometric hashing,” Pattern Recognition Letters, Vol.24, pp. 137-146, 2003.
  9. [9] S. Miyamoto, “Introduction to cluster analysis: theory and applications of fuzzy clustering,” Morikita-Shuppan, Tokyo, 1999.
  10. [10] J. C. Bezdek, “Pattern recognition with fuzzy objective function algorithm,” Plenum, New York, 1981.
  11. [11] R. N. Dave, “Characterization and Detection of Noise in Clustering,” Pattern Recognition Letters, Vol.12, pp. 657-664, 1991.
  12. [12] T. Irie, H. Katou, H.Maeda, “Object recognition from stereo images using synergetics and combinatorial optimization,” Proc. of SICE Annual Conf. 2003, in CD-ROM, pp. 1221-1226, 2003.
  13. [13] T. Calinski and Harabasz, “A dendrite method for cluster analysis,” Communication in Statistics, Vol.3, No.1, pp. 1-27, 1974.

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