A 3D Registration Method based on Optimal Segmentation and Local Character Tensors

Jingjing Wang; Hajime Nobuhara; Yutaka Hatakeyama; Fangyan Dong; Kaoru Hirota

doi:10.14864/softscis.2006.0.151.0

SCIS & ISIS 2006

Session ID : TH-G2-3

DOI https://doi.org/10.14864/softscis.2006.0.151.0

Conference information

Host: Japan SOciety for Fuzzy Theory and intelligent informatics

Co-host: The Korea Fuzzy Logic and Intelligent Systems Society, IEEE Computational Intelligence Society, The International Fuzzy Systems Association, 21th Century COE Program "Creation of Agent-Based Social Systems Sciences"

TH-G2 Video/3D Image Processing

A 3D Registration Method based on Optimal Segmentation and Local Character Tensors

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

Author information

Keywords: spin image, Gaussian Curvature operator, local character tensor

CONFERENCE PROCEEDINGS FREE ACCESS

Details

Abstract

An automatic 3D pair-wise registration method based on an optimal segmentation and a local character tensor is proposed. It consists of two characters, i.e., the optimal segmentation to realize the automatic processing and the local character tensor to improve the matching probability. Non-structured 3D datasets of a rabbit doll are tested on a PC with Intel Pentium M 1.60GHz and 512MB memory. Experiment results show the proposed method increases the matching probability average for 18.5% and decreases the computational time average for 21.6 second compared to those of the conventional spin image method.

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