
Proceedings Paper
Pattern recognition with composite correlation filters designed from noisy training imagesFormat | Member Price | Non-Member Price |
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Paper Abstract
Correlation filters for target detection are usually designed under the assumption that the appearance of a target
is explicitly known. Because the shape and intensity values of a target are used, correlation filters are highly
sensitive to changes in the target appearance in the input scene, such as those of due to rotation or scaling.
Composite filter design was introduced to address this problem by accounting for different possibilities for the
appearance of the target within the input scene. However, explicit knowledge for each possible appearance is
still required. In this work, we propose composite filter design when an object to be recognized is given in noisy
training images and its exact shape and intensity values are not explicitly known. Optimal filters with respect
to the peak-to-output energy criterion are derived and used to synthesize a single composite filter that can be
used for distortion invariant target detection. Parameters required for filter design are estimated with suggested
techniques. Computer simulation results obtained with the proposed filters are presented and compared with
those of common composite filters.
Paper Details
Date Published: 22 September 2011
PDF: 8 pages
Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 81350B (22 September 2011); doi: 10.1117/12.892879
Published in SPIE Proceedings Vol. 8135:
Applications of Digital Image Processing XXXIV
Andrew G. Tescher, Editor(s)
PDF: 8 pages
Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 81350B (22 September 2011); doi: 10.1117/12.892879
Show Author Affiliations
Pablo Mario Aguilar-González, Ctr. de Investigación Científica y de Educación Superior de Ensenada (Mexico)
Vitaly Kober, Ctr. de Investigación Científica y de Educación Superior de Ensenada (Mexico)
Published in SPIE Proceedings Vol. 8135:
Applications of Digital Image Processing XXXIV
Andrew G. Tescher, Editor(s)
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