An auto-adaptive neuro-fuzzy segmentation architecture is presented. The system consists of a multilayer perceptron (MLP) network that performs adaptive thresholding of the input image using labels automatically preselected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the system.
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