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Proceedings Paper

Fusion and optimized Gabor filter design for object detection
Author(s): David Weber; David P. Casasent
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Paper Abstract

We consider the problem of detection of objects in images using one filter based on different 2D Gabor functions. By detection, we mean locating multiple classes of targets with distortions present and in a clutter background. It is also desirable to minimize false alarms due to clutter. Gabor functions (GFs) are Gaussian functions modulated by complex sinusoids. The imaginary (real) part of a GF has been shown to be a good edge (blob) detector. In this work, we use a single filter which is a linear combination of the real and imaginary parts of several GFs. We refer to this as a macro Gabor filter. It is correlated with an input image and then thresholded to detect targets. The new aspects are: combining real and imaginary parts of GFs into one filter, separately optimizing the parameters of the GFs by controlling the shape of the correlation outputs for true classes and clutter and separately optimizing the linear combination coefficients using a new square law perceptron to detect hot and cold objects. We show multi-class distortion invariant detection results with better performance than obtained with other methods.

Paper Details

Date Published: 3 October 1995
PDF: 14 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222720
Show Author Affiliations
David Weber, Carnegie Mellon Univ. (United States)
David P. Casasent, 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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