Corners have been one of the most important features in computer vision since they are invariant to geometric transformations, such as translation, rotation and scaling. Boundary-based corner detectors, segmenting objects from an image first and then locating the discontinuities on the object boundaries, have been widely applied to polygonal approximation, spline curve fitting, automated visual inspection, image segmentation, image registration, shape morphing, handwriting/environment/object recognition, motion sketch, etc. The accuracy of corner detection on boundaries is primarily influenced by quantization and noises.
In this thesis, we propose a robust boundary-based corner detection algorithm for diverse images. The algorithm is composed of three components: a new measure of significance based on the eigenvalues of covariance matrices, threshold estimation of the measure of significance of any angle, and an optimization procedure based on a discriminant criterion for determining the length of region of support. The experimental results show that our algorithm outperforms other methods, even in the noisy samples. These robust results are due to not only the reliable measure of significance but also the discriminating optimization procedure of our algorithm.