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Proceedings Paper

Multiclass 3D distortion-invariant object detection in clutter
Author(s): Gregory P. House; David P. Casasent
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Paper Abstract

We consider distortion-invariant filters for detection (i.e. to locate a number of different object classes). For each object, there are two different depression angles, four different contrast ratios, and 18 different aspect views. The objects are present in a variety of different real background clutter. One filer is able to recognize (detect) all 2 X 4 X 18 X 5 equals 720 object versions in clutter with no false alarms using NT equals 36 training set images. The filter uses training objects in a constant background, correlation peak constraints on the NT objects, and minimizes a weighted combination of the correlation plane energy due to the distortion spectrum and a noise spectrum. The new object and noise models used produce this excellent performance with no false class clutter training.

Paper Details

Date Published: 1 March 1994
PDF: 10 pages
Proc. SPIE 2237, Optical Pattern Recognition V, (1 March 1994); doi: 10.1117/12.169412
Show Author Affiliations
Gregory P. House, Carnegie Mellon Univ. (United States)
David P. Casasent, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 2237:
Optical Pattern Recognition V
David P. Casasent; Tien-Hsin Chao, Editor(s)

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