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Proceedings Paper

Generalized feature detection using the Karhunen-Loeve transform and expansion matching
Author(s): Zhiqian Wang; Raghunath K. Rao; Dibyendu Nandy; Jezekiel Ben-Arie; Nebosja Jojic
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Paper Abstract

This paper presents a novel generalized feature extraction method based on the expansion matching (EXM) method and the Karhunen-Loeve (KL) transform. This yields an efficient method to locate a large variety of features with reduced number of filtering operations. The EX method is used to design optimal detectors for different features. The KL representation is used to define an optimal basis for representing these EXM feature detectors with minimum truncation error. Input images are then analyzed with the resulting KL bases. The KL coefficients obtained from the analysis are used to efficiently reconstruct the response due to any combination of feature detectors. The method is applied to real images and successfully extracts a variety of arc and edge features as well as complex junction features formed by combining two or more arc or line features.

Paper Details

Date Published: 27 February 1996
PDF: 7 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233257
Show Author Affiliations
Zhiqian Wang, Illinois Institute of Technology (United States)
Raghunath K. Rao, Illinois Institute of Technology (United States)
Dibyendu Nandy, Univ. of Illinois/Chicago (United States)
Jezekiel Ben-Arie, Univ. of Illinois/Chicago (United States)
Nebosja Jojic, Univ. of Illinois/Chicago (United States)

Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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