Share Email Print

Proceedings Paper

Multiscale Vector Fields for Image Pattern Recognition
Author(s): Kah-Chan Low; James M. Coggins
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We propose a uniform processing framework for low-level vision computing in which a bank of spatial filters maps the image intensity structure at each pixel into an abstract feature space. Some properties of the filters and the feature space will be described. Local orientation is measured by a vector sum in the feature space as follows: each filter's preferred orientation along with the strength of the filter's output determine the orientation and the length of a vector in the feature space; the vectors for all filters are summed to yield a resultant vector for a particular pixel and scale. The orientation of the resultant vector indicates the local orientation, and the magnitude of the vector indicates the strength of the local orientation preference. Limitations of the vector sum method will be discussed. Our investigations show that the processing framework provides a useful, redundant representation of image structure across orientation and scale.

Paper Details

Date Published: 1 March 1990
PDF: 11 pages
Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); doi: 10.1117/12.969731
Show Author Affiliations
Kah-Chan Low, University of North Carolina at Chapel Hill (United States)
James M. Coggins, University of North Carolina at Chapel Hill, Goddard Space Flight Center (United States)

Published in SPIE Proceedings Vol. 1192:
Intelligent Robots and Computer Vision VIII: Algorithms and Techniques
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top