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Proceedings Paper

Shift�Invariant Recognition Of Rotationatly Deforme' Ship Silhouettes At Multiple Resolution Scales
Author(s): Mark S. Schmalz; Frank M. Caimi
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Paper Abstract

A novel method for two-dimensional pattern recognition and feature extraction, applicable to microprocessor-based vision systems, is presented which employs fractal geometric analysis. Fractal contour transformation and transform correlation techniques are discussed in relation to their effectiveness in classifying rotationally deformed images over a wide resolution range. vractal geometric analysis exhibits several attributes: 1. position-, size-, and rotation-invariance is preserved in the absence of image coordinate transformation, 2. invariance to out-of-plane rotation is exhibited over the range ±60° of broadside, and 3. out-of-plane rotation can be computed from imagery and quantified in terms of the fractal dimension. This work is supported by experimental verification of a ship silhouette recognition algorithm. Results are presented in terms of recognition ratio and computational load.

Paper Details

Date Published: 15 October 1986
PDF: 13 pages
Proc. SPIE 0638, Hybrid Image Processing, (15 October 1986); doi: 10.1117/12.964279
Show Author Affiliations
Mark S. Schmalz, Harbor Branch Foundation (United States)
Frank M. Caimi, Harbor Branch Foundation (United States)


Published in SPIE Proceedings Vol. 0638:
Hybrid Image Processing
David P. Casasent; Andrew G. Tescher, Editor(s)

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