MATLAB Implementation of SUSAN Algorithm for Corner Detection

The SUSAN algorithm is a widely used method for corner detection, offering multiple implementation approaches in MATLAB. One common implementation involves calculating grayscale differences between a pixel and its surrounding neighborhood pixels to determine corner positions - this typically uses a circular mask template and compares intensity values against a brightness threshold. Another approach utilizes gradient information from the image to detect corners, often employing convolution operations with specially designed kernels to enhance corner response. In MATLAB implementations, key functions like imfilter for image filtering and conv2 for 2D convolution are frequently used to process the SUSAN mask responses. Regardless of the implementation method chosen, the SUSAN algorithm effectively identifies corner features in images through its distinctive nucleus similarity concept, providing robust corner detection even under noise conditions. The algorithm's core principle involves analyzing the USAN (Univalue Segment Assimilating Nucleus) area where pixels share similar intensity values with the central nucleus point.