MODEL GENERATION AND MODEL-MATCHING OF REAL IMAGES BY A FUZZY APPROACH

A method based on fuzzy sets for model representation and matching of real images in a knowledge-based system is presented. The detailed descriptions of the system's models, data structures, and matching mechanism, as well as the introduction to a method for the generation of symbolic models, are the main topics of the present paper. Models and data structures are based on the use of fuzzy restrictions. The process of model generation starts from a set of training images whose features are analysed to find discriminant descriptions of single objects and their mutual relationships. As an example of application, the efficiency of this approach has been tested using medical tomographic images acquired by the magnetic resonance technique. Results demonstrate the applicability of one ideal model to various real scenes of the same type. The system's performance and the errors incurred are evaluated. The robustness of the model and of the method has been proved both by processing images affected by noise and by changing segmentation threshold values in the preprocessing step.