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16 September 2005 Recognition of partially occluded objects using correlation filters with training
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Abstract
One of the main problems in visual signal processing is incomplete information owing an occlusion of objects by other objects. It is well known that correlation filters mainly use contour information of objects to carry out pattern recognition. However, in real applications object contours are often disappeared. In these cases conventional correlation filters without training yield a poor performance. In this paper two novel methods based on correlation filters with training for recognition of partially occluded objects are proposed. The methods improve significantly discrimination capability of conventional correlation filters. The first method performs training of a correlation filter with both a target and objects to be rejected. In the second proposal two different correlation filters are designed. They deal independently with contour and texture information to improve recognition of partially occluded objects. Computer simulation results for various test images are provided and discussed.
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J. Angel González-Fraga, Vitaly Kober, and Josue Álvarez-Borrego "Recognition of partially occluded objects using correlation filters with training", Proc. SPIE 5909, Applications of Digital Image Processing XXVIII, 59091X (16 September 2005); https://doi.org/10.1117/12.615796
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