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Proceedings Paper

Algorithm fusion for detection with reduced PFA
Author(s): David P. Casasent; Anqi Ye; Ashit Talukder
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Paper Abstract

We consider detection (locating all objects in a scene) independent of object distortions and contrast differences and in the presence of clutter. We employ several different new detection algorithms; to reduce false alarms. We fuse (combine) the outputs from different detection algorithms. We describe a new peak sorting detection scoring algorithm and 3 different fusion algorithms to combine the results from different algorithms: binary, analog, and hierarchial fusion. Quantitative data on a distortion-invariant six object class is presented; the objects have a wide range of object contrasts including obscured objects and the objects are present in severe clutter.

Paper Details

Date Published: 15 March 1996
PDF: 8 pages
Proc. SPIE 2752, Optical Pattern Recognition VII, (15 March 1996); doi: 10.1117/12.235652
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
Anqi Ye, Carnegie Mellon Univ. (United States)
Ashit Talukder, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 2752:
Optical Pattern Recognition VII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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