Share Email Print

Proceedings Paper

Fast Adaptive Algorithms For Low-Level Scene Analysis: Applications Of Polar Exponential Grid (PEG) Representation To High-Speed, Scale-And-Rotation Invariant Target Segmentation
Author(s): P. S. Schenker; K. M. Wong; E. G. Cande
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

This paper presents results of experimental studies in image understanding. Two experiments are discussed, one on image correlation and another on target boundary estimation. The experiments are demonstrative of polar exponential grid (PEG) representation, an approach to sensory data coding which the authors believe will facilitate problems in 3-D machine perception. Our discussion of the image correlation experiment is largely an exposition of the PEG-representation concept and approaches to its computer implementation. Our presentation of the boundary finding experiment introduces a new robust stochastic, parallel computation segmentation algorithm, the PEG-Parallel Hierarchical Ripple Filter (PEG-PHRF).

Paper Details

Date Published: 12 November 1981
PDF: 11 pages
Proc. SPIE 0281, Techniques and Applications of Image Understanding, (12 November 1981); doi: 10.1117/12.965731
Show Author Affiliations
P. S. Schenker, Brown University (United States)
K. M. Wong, Brown University (United States)
E. G. Cande, Brown University (United States)

Published in SPIE Proceedings Vol. 0281:
Techniques and Applications of Image Understanding
James J. Pearson, Editor(s)

© SPIE. Terms of Use
Back to Top