Applying fuzzy logic in the modified single-layer perceptron image segmentation network

Abstract

In this paper, we propose a fuzzy-logic-based modified single layer perceptron (MSLP) image segmentation network for object extraction. We select a sigmoid gray level transfer function with the aid of the input image histogram and map the input gray levels into the interval [0,1]. Then we adopt the linear index of fuzziness of the output nodes as the error function of the image segmentation system to incorporate the learning capability of a neural network. Our scheme can successfully extract objects from the background. To further enhance the capability of the segmentation system, the proposed network is incorporated with fuzzy if-then rules to adaptively adjust the threshold of the activation function of the MSLP output neuron for best matching the local characteristics of the image. Fuzzy if-then rules involving the edge intensities and vertical positions of pixels are reasoned to determine the threshold adaptively. From the results of segmenting forward looking infrared (FLIR) images, better segmentation images have been obtained by incorporating fuzzy if-then rules with the MSLP segmentation technique. As demonstrated by this study, it is promising and worthy of study that incorporating human knowledge in terms of Fuzzy rules into a designed numerical algorithm can further improve performance, not only in the segmentation problem we present.