A Study on Fuzzy Logic in Computation Approach using Various Techniques

Fuzzy logic can assist in converting these uncertainties into knowledge or information in these kinds of circumstances. This data can then be handled using fuzzy set theory methods. When creating a pattern recognition system, the task of feature selection is crucial, but compared to classification or clustering, fuzzy set theory research in this field has not been as significant. Numerous fields, including speech recognition, images from remote sources, medical imaging, and atmospheric sciences, have reported applications of fuzzy pattern recognition and image processing. This results in the acquisition, pre- processing, segmentation, and extraction of the global features from the query image. The last step is the application of statistical categorization techniques. This group of algorithms works best with homogenous objects since they can be divided into segments with ease. There are common holistic techniques in. Although the holistic approaches are quick and easy, there are restrictions on detection when lighting and stance changes occur. However, local feature-based techniques work better with textured objects and are more resistant the notion that an object can be represented by a group of locally invariant patches. The stages involved in local feature-based approaches often start with the extraction of prominent spots. This study discusses about fuzzy logic in computation approach using various techniques.