Performance analysis of corner detection algorithms based on edge detectors

Detecting corner locations in images plays a significant role in several computer vision applications. Among the different approaches to corner detection, contour-based techniques are specifically interesting as they rely on edges detected from an image, and for such corner detectors, edge detection is the first step. Almost all the contour-based corner detectors proposed in the last few years use the Canny edge detector. There is no comparative study that explores the effect of using different edge detection method on the performance of these corner detectors. This paper fills that gap by carrying out a performance analysis of different contour-based corner detectors when using different edge detectors. We studied four recently developed corner detectors, which are considered as current state of the art and found that the Canny edge detector should not be taken as a default choice and in fact the choice of edge detector can have a profound effect on the corner detection performance. We examined commonly used predefined threshold-based Canny detector with the adaptive Canny detector and found that adaptive Canny detector gives better results to work with.