Fuzzy Logic Based Gray Image Extraction and Segmentation

Image segmentation and subsequent extraction from a noise-affected background, has all along remained a challenging task in the field of image processing. There are various methods reported in the literature to this effect. These methods inc lude various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. Providing an extraction solution working in unsupervised mode happens to be even more interesting a problem. Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE) and Peak Signal to Noise Ratio (PSNR). Index Terms Fuzzy Rule Base, Image Extraction, Fuzzy Inference System (FIS), Membership Functions, Threshold methods, Soft Computing, Fuzzy Image Processing, Feature based modeling ——————————  ——————————