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Proceedings Paper

Adaptive SDF filters for recognition of partially occluded objects
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Paper Abstract

One of the main problems in visual image processing is incomplete information owing an occlusion of objects by other objects. Since correlation filters mainly use contour information of objects to carry out pattern recognition then conventional correlation filters without training often yield a poor performance to recognize partially occluded objects. Adaptive correlation filters based on synthetic discriminant functions for recognition of partially occluded objects imbedded into a cluttered background are proposed. The designed correlation filters are adaptive to an input test scene, which is constructed with fragments of the target, false objects, and background to be rejected. These filters are able to suppress sidelobes of the given background as well as false objects. The performances of the adaptive filters in real scenes are compared with those of various correlation filters in terms of discrimination capability and robustness to noise.

Paper Details

Date Published: 24 August 2006
PDF: 10 pages
Proc. SPIE 6312, Applications of Digital Image Processing XXIX, 63121G (24 August 2006); doi: 10.1117/12.678555
Show Author Affiliations
J. Ángel González-Fraga, CICESE (Mexico)
Vitaly Kober, CICESE (Mexico)
Josué Álvarez-Borrego, CICESE (Mexico)


Published in SPIE Proceedings Vol. 6312:
Applications of Digital Image Processing XXIX
Andrew G. Tescher, Editor(s)

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