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Proceedings Paper

Invariants from the three-dimensional vector autoregressive model
Author(s): Jun Fujiki; Yasuhiko Kiuchi; Masaru Tanaka; Taketoshi Mishima
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Paper Abstract

The invariance and covariance of extracted features from an object under certain transformation play quite important roles in the fields of pattern recognition and image understanding. For instance, in order to recognize a three dimentional object, we need specific features extracted from a given object. These features should be independent of the pose and the location of an object. To extract such feature, The authors have presented the three dimensional vector autoregressive model (3D VAR model). This 3D VAR model is constructed on the quaternion, which is the basis of SU(2) (the rotation group in two dimensional complex space). Then the 3D VAR model is defined by the external products of 3D sequential data and the autoregressive(AR) coefficients, unlike the usual AR models. Therefore the 3D VAR model has some prominent features. For example, The AR coefficients of the 3D VAR model behave like vectors under any three dimensional rotation. In this paper, we derive the invariance from 3D VAR coefficients by inner product of each 3D VAR coefficient. These invariants make it possible to recognize the three dimensional curves.

Paper Details

Date Published: 2 November 2001
PDF: 12 pages
Proc. SPIE 4476, Vision Geometry X, (2 November 2001); doi: 10.1117/12.447269
Show Author Affiliations
Jun Fujiki, National Institute of Advanced Industrial Science and Technology (Japan)
Yasuhiko Kiuchi, Saitama Univ. (Japan)
Masaru Tanaka, Saitama Univ. (Japan)
Taketoshi Mishima, Saitama Univ. (Japan)

Published in SPIE Proceedings Vol. 4476:
Vision Geometry X
Longin Jan Latecki; David M. Mount; Angela Y. Wu; Robert A. Melter, Editor(s)

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