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JACIII Vol.11 No.7 pp. 848-857
doi: 10.20965/jaciii.2007.p0848
(2007)

Paper:

Local Character Tensors for 3D Registration Method on Free-View Datasets

Jingjing Wang*, Fangyan Dong*, Yutaka Hatakeyama*,
Hajime Nobuhara**, and Kaoru Hirota*

*Dept. of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan

**Dept. of Intelligent Interaction Technologies, University of Tsukuba, Tsukuba Science City, Japan

Received:
January 16, 2007
Accepted:
May 2, 2007
Published:
September 20, 2007
Keywords:
3D registration, Sutherland Hodgman segmentation, pair-registration, tensor, matching
Abstract

A local character tensor is proposed for the automatic three-dimensional (3D) pair-wise registration based on free-view 3D datasets. In the proposed method, there are two characters, i.e., the optimal segmentation to realize the automatic processing and local character tensor to improve the matching probability. It is applied for solving the mismatching problem and large-scale 3D datasets, using non-structured datasets are tested in a PC with Intel Pentium M 1.50 GHz and 1.0 GB memory. Pair-wised experimental results show the proposed method increases average 12.6% matching probability and decreases average 18.9 seconds computational time compared to the conventional local character based registration method. This registration method can be further applied to 3D reconstruction from navigation, model based object recognition to accurate 3D geometric object model application.

Cite this article as:
Jingjing Wang, Fangyan Dong, Yutaka Hatakeyama,
Hajime Nobuhara, and Kaoru Hirota, “Local Character Tensors for 3D Registration Method on Free-View Datasets,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.7, pp. 848-857, 2007.
Data files:
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