An iterative strategy to approach corners using a new saliency measurement

In this paper we propose a novel corner detection algorithm using local adaptive thresholding and iterative approaching. First, a new metric is defined to measure the saliency of the corner responses within a local region. With this metric, the task to find corner points is transformed into a task to find those regions with high saliency of corner responses. Then an iterative strategy is adopted to gradually narrow down these regions and finally approaching the actual positions of true corners. We demonstrate that the proposed algorithm is equivalent to a local adaptive thresholding method and thus can effectively suppress edge points. Experiments show that both its false detection rate and miss detection rate are lower than those of Noble detector, and its average detection accuracy is higher than the latter by 3.58%.