Linear signal decomposition approach to affine-invariant contour identification

David Cyganski; Richard F. Vaz

doi:10.1117/12.57050

1 February 1992 Linear signal decomposition approach to affine-invariant contour identification

David Cyganski, Richard F. Vaz

Proceedings Volume 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques; (1992) https://doi.org/10.1117/12.57050
Event: Robotics '91, 1991, Boston, MA, United States

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.

Citation Download Citation

David Cyganski and Richard F. Vaz "Linear signal decomposition approach to affine-invariant contour identification", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); https://doi.org/10.1117/12.57050

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available