SUSAN Corner Detection Algorithm Implementation in MATLAB

In digital image processing, the SUSAN corner detection algorithm serves as a fundamental technique. This algorithm is efficiently implemented in the MATLAB environment, leveraging matrix operations for optimal performance. The SUSAN algorithm detects corners by analyzing the rate of change in image intensity values through a circular mask approach. The core implementation involves calculating the USAN (Univalue Segment Assimilating Nucleus) area using pixel intensity comparisons within a 37-pixel circular template. Key MATLAB functions like im2double for image normalization and nlfilter for neighborhood operations are typically employed. This algorithm demonstrates excellent detection capabilities for both edges and corners in images, with the implementation often including threshold parameters to control sensitivity. By utilizing the SUSAN algorithm, practitioners can effectively extract and analyze critical target features in digital images, thereby providing robust support for subsequent image processing tasks such as feature matching and object recognition. The MATLAB code typically includes functions for template generation, intensity comparison, and corner response calculation, making it suitable for both educational and industrial applications.