Automated thresholding for low-complexity corner detection

Widely-used corner detectors such as Shi-Tomasi and Harris necessitate the selection of a threshold parameter manually in order to identify good quality corners. The recent attempts based on trial-and-error methods for threshold setting are time-consuming, making them unsuitable for low-cost and embedded video processing applications. In this paper we propose a novel automated thresholding technique for Shi-Tomasi and Harris corner detectors based on an iterative pruning strategy. The proposed pruning strategy involves the rapid extraction of potential corner regions and their evaluation for detecting corners. This pruning strategy is applied iteratively until the required number of corners is identified without necessitating the selection of the threshold parameter. As the complex corner measure computations of the Shi-Tomasi and Harris detectors are only applied to very small regions selected by the proposed pruning method, significant savings in computation is also achieved. In addition, the pruning strategy is computationally simpler, making it suitable for deployment in low cost and embedded applications. Our evaluations on the NiOS-II embedded platform show that the proposed automated thresholding technique is able to achieve an average speedup of 67% in Shi-Tomasi and 51% in Harris, with almost no loss in accuracy. The proposed method to identify corners without the manual selection of a threshold parameter makes it ideal for corner detection on a wide range of imagery where the quantity and quality of corners is not known a priori such as in video processing applications.