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Proceedings Paper

Invariant correlation of objects using non-linear composite filters
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Paper Abstract

In this work we use non linear composite filters in object recognition, even when they have rotation, scale and noise distortions. We generated 936 images of the letters E, F, H, P and B. The images consisted of these letters scaled from 70% to 130% and rotated 360°. The maximum number of images supported by these filters was determined by a numerical experiment. This was done by generating filters with different amount of images each. We have images at 13 scales and each scale with 72 different angles, tests were done to two different kinds of filters, one where all the scales were present and we add more angles to increase the number of images, and another where all of the angles were present and more scales were added to increase the number of images. Considering a system confidence level of at least 80%, the maximum number of images allowed by the filter is around 216. In one type of filter we have the letter rotated 360°. We found a "rotation problem", since circles were introduced in the Fourier plane, in other words first order Bessel functions were introduced in the image spectrum, which creates complications when working with images that also have circles in their spectrum. Due to this we propose a segmented filter which breaks the circular symmetry. Non-linear composite filters can recognize the target in presence of distortions.

Paper Details

Date Published: 15 September 2008
PDF: 11 pages
Proc. SPIE 7073, Applications of Digital Image Processing XXXI, 707326 (15 September 2008); doi: 10.1117/12.795140
Show Author Affiliations
Ricardo Enrique Guerrero-Moreno, Ctr. de Investigación Científica y de Educación Superior de Ensenada (Mexico)
Josué Álvarez-Borrego, Ctr. de Investigación Científica y de Educación Superior de Ensenada (Mexico)

Published in SPIE Proceedings Vol. 7073:
Applications of Digital Image Processing XXXI
Andrew G. Tescher, Editor(s)

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