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 include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods, et cetera. 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. This chapter proposes 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, the author takes recourse to effective metrices like Mean Squared Error (MSE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR).
ON-LINE CONSTRUCTION AND RULE BASE SIMPLIFICATION BY REPLACEMENT IN FUZZY SYSTEMS APPLIED TO A WASTEWATER TREATMENT PLANT