Adaptive nonlinear composite filters for pattern recognition
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Adaptive composite nonlinear filters for reliable illumination-invariant pattern recognition are proposed. The information about objects to be recognized, false objects, and a background to be rejected is utilized in an iterative training procedure to design a nonlinear adaptive correlation filter with a given value of discrimination capability. The designed filter during recognition process adapts its parameters to local statistics of the input image. Computer simulation results obtained with the proposed filters in test nonuniform illuminated scenes are discussed and compared with those of linear composite correlation filters in terms of recognition performance.
KeywordsTraining Image Mean Absolute Error Impulsive Noise Nonlinear Correlation Discrimination Capability
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