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Proceedings Paper

Linear signal decomposition approach to affine-invariant contour identification
Author(s): David Cyganski; Richard F. Vaz
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Paper Abstract

A means for the identification of objects from contours despite affine transform induced distortions that takes the form of a linear signal space decomposition has been obtained. This new technique also yields robust estimates of the affine transformation from which the 3-D rotations of a near planar object may be obtained. The ability to determine object identity and orientation from a singe model representation without iteration or combinatorial search proceeds from the use of affine invariant differential measures that may be derived via Lie group theory. The resulting technique is extremely robust in the presence of noise (or nonplanarity of the object) owing to the error rejection properties of the signal space projection operations. The resulting algorithm is amenable to high-speed implementation with digital signal processing hardware architectures because it can be reduced to a sequence of linear 1-D signal processing operations. Included in this paper are a number of demonstration results that illustrate the resilience of the solutions in the presence of severe nonaffine distortion and pixelization error.

Paper Details

Date Published: 1 February 1992
PDF: 12 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57050
Show Author Affiliations
David Cyganski, Worcester Polytechnic Institute (United States)
Richard F. Vaz, Worcester Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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