SUSAN Operator for Image Edge and Corner Detection

The SUSAN (Smallest Univalue Segment Assimilating Nucleus) operator is an algorithm designed for detecting image edges and corners. It operates by comparing pixel intensity variations within a circular mask around each nucleus point, where the core implementation involves calculating the USAN (Univalue Segment Assimilating Nucleus) area - the region sharing similar brightness with the central pixel. A key parameter is the brightness threshold, which determines sensitivity to intensity differences. The algorithm identifies edges when the USAN area is half the mask size, and corners when it drops below a quarter. Users can customize detection templates by adjusting the mask radius and threshold values, allowing adaptation to specific corner types (e.g., sharp vs. rounded corners). This flexibility enables precise corner localization through code modifications like optimizing threshold values for noise reduction or implementing non-maximum suppression for refined edge detection. In practice, the SUSAN operator's efficiency stems from its simple arithmetic operations, making it suitable for real-time applications. Developers can implement it using nested loops for mask traversal or optimize with parallel processing for large images. Its applications span image processing and computer vision tasks, including feature matching, 3D reconstruction, and object recognition, where robust corner detection is crucial.