Type-2 Fuzzy Logic for Edge Detection of Gray Scale Images
Abdullah Gubbi, Mohammad Fazle · InTech eBooks · 2012
Fuzzy logic can be built on top of the experience of experts.Fuzzy logic relies upon the experience of experts who already have familiarity and understanding about the functionality of systems. Fuzzy logic can model nonlinear functions of arbitrary complexity.A fuzzy system can be created to match any set of input-output data.This process is made particularly easy by adaptive techniques like Adaptive Neuro-Fuzzy Inference Systems (ANFIS), which are available in Fuzzy Logic Toolbox. www.intechopen.comFuzzy Inference System -Theory and Applications 280 Fuzzy logic can be blended with conventional control techniques.Fuzzy systems don't necessarily replace conventional control methods.In many cases fuzzy systems augment them and simplify their implementation.Fuzzy logic is based on natural language.The basis for fuzzy logic is the basis for human communication.This observation underpins many of the other statements about fuzzy logic.Because fuzzy logic is built on the structures of qualitative description used in everyday language, fuzzy logic is easy to use.Natural language, which is used by ordinary people on a daily basis, has been shaped by thousands of years of human history to be convenient and efficient.Sentences written in ordinary language represent a triumph of efficient communication [Webpage3].The most important reasons for FIP are as follows:1. Fuzzy techniques are powerful tools for knowledge representation and processing 2. Fuzzy techniques can manage the vagueness and ambiguity efficientlyIn many image-processing applications, expert knowledge is used to overcome the difficulties in object recognition, scene analysis, etc. Fuzzy set theory and fuzzy logic offer us powerful tools to represent and process human knowledge in the form of fuzzy IF-THEN rules.On the other side, many difficulties in image processing arise because of the uncertain nature of the data, tasks, and results.This uncertainty, however, is not always due to the randomness but to the ambiguity and vagueness.Beside randomness which can be managed by probability theory, FIP can distinguish between three other kinds of imperfection in the image processing. Grayness ambiguity Geometrical fuzziness Vague (complex/ill-defined) knowledgeThese problems are fuzzy in the nature.The question whether a pixel should become darker or brighter after processing than it is before?Where is the boundary between two image segments?What is a tree in a scene analysis problem?All of these and other similar questions are examples for situations that a fuzzy approach can be applied in a more suitable way to manage the imperfection.FIP is an amalgamation of different areas of fuzzy set theory, fuzzy logic and fuzzy measure theory.The most important theoretical components of fuzzy image processing: