Corner detection by a cost minimization approach

Abstract Corner detection, which is a valuable tool in biological and machine vision systems, is cast as a problem of cost optimization. The cost function is suitably devised to capture different desirable characteristics of corners such as edginess, curvature and region dissimilarity. The geometrical structure of the corner as well as the gray level variation of the image are accounted for in cost factors to evaluate the quality of corner configurations. The cost function is minimized using a simulated annealing algorithm. This approach also provides corner orientations and angles in addition to corner locations. The efficacy of the approach is demonstrated by experimental results.