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• W2182578556 abstract "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." @default.
• W2182578556 created "2016-06-24" @default.
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• W2182578556 date "2014-01-01" @default.
• W2182578556 modified "2023-10-01" @default.
• W2182578556 title "Rotation-Invariant Nonlinear Filters Design" @default.
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• W2182578556 doi "https://doi.org/10.1007/978-3-319-12568-8_2" @default.
• W2182578556 hasPublicationYear "2014" @default.
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