An adaptive implementation of the SUSAN method for image edge and feature detection

Edge and feature points are basic low level primitives for image processing. The justification is that such points convey a lot of structural information about the surfaces of the objects captured in the imaging process. Because of this, edge and feature detection are two of the most common operations in image analysis. Despite the variety of operators available for these purposes, there are still some drawbacks common to most of them, even the more sophisticated ones. Among them, the problems of connectivity and shape of junctions by edge detectors, and the dichotomy between reliability of detection and localization accuracy for feature detectors are particularly outstanding. The present work proposes an adaptive implementation of the SUSAN method for edge and feature detection. The aim is the development of an algorithm that offers some performance improvement in tackling these limitations