A comparative study of different corner detection methods

Abstract

Interest points are widely used in computer vision applications such as camera calibration, robot localization and object tracking that require fast and efficient feature matching. A large number of techniques have been proposed in the literature. This paper evaluates the state of art techniques for interest point detection including execution time and suitability for real time applications. Such comparative study is crucial for specific applications, since it is always necessary to understand the advantages and disadvantages of the existing techniques so that best possible ones can be selected. The comparative study shows that: (1) the CSS method performs best in corner extraction. It is the fast and the most reliable and has the lowest noise sensitivity with the highest true corner detection rate, even though it still detects some false corners; (2) SUSAN detector would be the second choice and is acceptable and useful in applications requiring a computationally efficient detector and working on a restricted set of images.