A Novel Window-Based Corner Detection Algorithm for Gray-Scale Images

In computer vision, the corners of an object play an important role in shape representation and analysis. In this paper, we describe a new approach to corner detection in a digital image based on the assumption that corners are image points with high information content, and hence corners in an image exist in the regions having considerably high intensity variations. Consequently, a complex corner response function (CRF) is computed only within the regions with considerable high intensity variation instead of entire image reducing the computational cost of the whole procedure. Experiments are conducted with the help of a few images showed the efficiency of the technique, both in terms of execution time and false positive corners.