System modeling is an essential step in decision-making process in order to explain, predict and control a system. Better tools for analyzing the data lead to better modeling of the system and hence, better solutions for the decision problems. Amongst the various system modeling techniques, fuzzy system modeling (FSM) provides the decision maker with valuable knowledge (linguistic variables). The most beneficial step throughout a FSM procedure is to detect the underlying data structure and to translate it into a collection of fuzzy rules. Therefore, fuzzy rule-base structure (the basis of the FSM) tries to identify the underlying relationship between input and output variables of a fuzzy system. However, FSM methods have major drawbacks of subjectivity and suffer lack of generalization. To overcome these issues, reduce expert's knowledge intervention and rather build self-learning systems, developed an enhanced FSM approach, called fuzzy function, which does not require construction of fuzzy rules, has less computation steps with less complexity.
This API is a Type-II fuzzy image processing expert system based on Type-II fuzzy function to recognize patterns in images. As mentioned in  Type-2 Fuzzy Image Processing System, this approach has four main steps: