Pattern Recognition in Blur Motion Noisy Images using Fuzzy Methods for Response Integration in Ensemble Neural Networks
Miguel López, Patricia Melín, Oscar Castillo · 2009
Abstract—Linear Blur Motion is one of the most common degradation functions that corrupt images. Since 1976 many researchers have tried to estimate blur motion parameters and this problem can be solved for noise free images but in the case of noisy images this can be done when the image SNR is low. In this paper, we consider pattern recognition with ensemble neural networks for the case of fingerprints; we propose the use of fuzzy methods for Response Integration in Ensemble Neural Networks for blur motion noisy images. An ensemble neural network of three modules is used; each module is a local expert on person recognition based on a biometric measure (the fingerprints). The Response Integration method of the ensemble neural networks has the goal of combining the responses of the modules to improve the recognition rate of the individual modules when the SNR rate blur motion signal increases to a high level.