LAUSR

As one of the most significant image local features, corner is widely utilized in many computer vision applications. A number of contour-based corner detection algorithms have been proposed over the last decades, among which the chord-to-point distance accumulation (CPDA) corner detector is reported to produce robust performance in corner detection, especially compared with curvature scale-space (CSS) based corner detectors, which are sensitive to local variation and noise on the contour. In this paper, we investigate the CPDA algorithm in terms of its limitations, and then propose the altitude-to-chord ratio accumulation (ACRA) corner detector based on CPDA approach. Altitude-to-chord ratio is insensitive to the selection of chord length compared with chord-to-point distance, which allows us utilize a single chord instead of the three chords used in CPDA algorithm. Besides, we replace the maximum normalization used in CPDA algorithm with the linear normalization to avoid the uneven data projection. Numerical experiments demonstrate that the proposed ACRA corner detection algorithm outperforms the CPDA approach and other seven state-of-the-art methods in terms of the repeatability and localization error evaluation metrics.