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Geometrical distortions are a major problem in image recognition. Composite correlation filters can be used for distortion-invariant image recognition by incorporating rotated versions of the target object. Traditionally composite filters are designed with linear techniques; but, these filters are sensitive to non-Gaussian noise. On the other hand, for the same purpose, composite nonlinear filters have been proposed too. These filters have a good discrimination capability and they are robust to non-Gaussian noise and illumination changes; however, the performance of filter could be reduced when the number of training images incorporated increases. In this paper, we propose a method for designing rotation-invariant composite nonlinear filters. The method tries to maximize the number of objects incorporated into the filter and preserve its performance.

Rotation-Invariant Nonlinear Filters Design