Powerful Corner Detection Algorithms

This document presents powerful corner detection programs that significantly enhance corner identification capabilities in image processing and computer vision applications. The implementation includes robust algorithms such as Harris corner detection and SUSAN corner detection, each featuring distinct computational approaches. The Harris corner detection algorithm operates as a derivative-based method that calculates the rate of change in pixel intensity values using the structure tensor matrix, typically implemented through eigenvalue analysis of the autocorrelation matrix. Key implementation steps involve computing image gradients, building the M matrix, and applying response function R = det(M) - k*trace(M)^2. Meanwhile, the SUSAN (Smallest Univalue Segment Assimilating Nucleus) corner detection algorithm employs a density-based approach that evaluates pixel density within a circular mask by comparing nucleus intensity with its neighbors using the USAN (Univalue Segment Assimilating Nucleus) principle. Critical implementation aspects include adaptive thresholding and morphological analysis of the USAN area. These corner detection algorithms demonstrate extensive applicability in computer vision and image processing domains, with ongoing research continuously exploring their optimization potential through techniques like non-maximum suppression and scale-space adaptation for improved robustness and accuracy.