Fuzzy-logic-based modified single-layer perceptron segmentation network

Abstract

In this paper, we propose a modified singlelayer perceptron (MSLP) segmentation network for object extraction. We select a sigmoid gray level transfer function from the histogram of the input image 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 multiple objects with different gray levels. 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 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 result of segmenting the forward looking infrared (FLIR) image, a better segmentation image has been obtained by incorporating fuzzy if-then rules with the MSLP segmentation technique.