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Proceedings Paper

Detection algorithm fusion concepts for computer vision
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 hierarchical 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: 3 October 1995
PDF: 8 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222659
Show Author Affiliations
David P. Casasent, Carnegie Mellon Univ. (United States)
Anqi Ye, Telxon Metanetics (United States)
Ashit Talukder, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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