Corner detection algorithm based on multifeature
Kunhua Zhang, Jingru Wang, Qiheng Zhang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
The corner is an important local feature of the image. This paper proposes a new algorithm for corner detection by multi-feature-intensity and edge points. A fast adaptive SUSAN principle based on local gray-level feature is presented firstly for detecting candidate corners. This improved method can detect the features in different contrast images automatically and fast through a self-adjust threshold, which is determined by the intensity gradient magnitude. To detect the corners on blurry edges, the candidate corners would include some edge points as a result of reducing the detection threshold. After these candidate corners are arrayed along the boundary trend by the method of edge element, the angles between approximate straight edge lines are calculated. Then the edge points contained in the candidate corners are removed since they have not significant discontinuous changes in the direction of boundary, and the false corners due to quantization are also removed by our method. In this way, the true corners are reserved. The experimental results showed that the proposed algorithm has good capability of corner detection and localization for different contrast image.