An Effective and Efficient Contour-based Corner Detector using Simple Triangular Theory

Abstract

Corner detection is an important operation in many computer vision applications. Among the contour-based corner detectors in the literature, the Chord-to-Point Distance Accumulation (CPDA) detector is reported to have one of the best repeatability and lowest localization error. However, we found that CPDA detector often fails to accurately detect the true corners in some situations. Furthermore, CPDA detector is also computationally expensive. To overcome these weaknesses of CPDA detector, we propose an effective but yet efficient corner detector using a simple triangular theory. Our experimental results show that our proposed detector outperforms CPDA and six other existing detectors in terms of repeatability. Our proposed detector also has one of the lowest localization error. Finally it is computationally the most efficient.