Handling of impreciseness in gray level corner detection using fuzzy set theoretic approach

Reliable corner detection is an important task in determining shape of different regions in an image. To detect corners in a gray level image under imprecise information, an algorithm based on fuzzy set theoretic model is proposed. The uncertainties arising due to various types of imaging defects such as blurring, illumination change, noise, etc., usually result in missing of significant curvature junctions (corners). Fuzzy set theory based modeling is well known for efficient handling of impreciseness. In order to handle the incompleteness arising due to imperfection of data, it is reasonable to model image properties in fuzzy frame work for reliable decision making. The robustness of the proposed algorithm is compared with well known conventional detectors. The performance is tested on a number of benchmark test images to illustrate the efficiency of the algorithm.