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Proceedings Paper

Three-dimensional autoregressive model under rotation
Author(s): Masaru Tanaka
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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 3D object, we need specific feature extracted from a given object. These features should be independent of the pose and the location of an object. In this paper, as one of the feature extracting methods, we present 3D autoregressive model and its higher dimensional extensions. 1D and 2D autoregressive model has been considered as one of the feature extracting methods.

Paper Details

Date Published: 30 September 1996
PDF: 8 pages
Proc. SPIE 2826, Vision Geometry V, (30 September 1996); doi: 10.1117/12.251790
Show Author Affiliations
Masaru Tanaka, National Research Council Canada (Canada)


Published in SPIE Proceedings Vol. 2826:
Vision Geometry V
Robert A. Melter; Angela Y. Wu; Longin Jan Latecki, Editor(s)

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