Generation of Entropy Based Binary Random Fields for Image Bundary Detection Based on Fuzzy Semantic Rules

This work presents a high resolution image classification by generating binary random fields based on fuzzy semantic rules by means of descriptors such as form, texture and relations between objects and sub-objects. Fuzzy systems are capable of representing diverse, non exact, uncertain and inaccurate knowledge or information. They use qualifiers that are very close to the human way of expressing knowledge, such as bright, medium dark, dark etc. Fuzzy systems can represent complex knowledge and even knowledge from contradictory sources. They are based on fuzzy logic, which represents a powerful approach to decision making. . The images are derived from multiresolution segmentation. It allows a creation of different levels of segments supporting a hierarchical structure, generating spatial relations between objects and subobjects.