Abstract
Corner is widely utilized in computer vision and image processing. As a representative contour-based corner detection algorithm, RJ detector is first proposed to use the K-cosine to estimate curvature of digital curves for corner finding. However, such influential approach is quite sensitive to the geometric transformations and noise due to its dynamic smoothing scale. To overcome this drawback and enhance its performance further, this paper presents a multi-scale version of RJ detector. First, we adopt fixed region of radius (RoS) to avoid its sensitiveness to geometric transformations; second, the technique of scale product is employed to enhance curvature extreme peaks and suppress noise for improving localization. Extensive experiments on several corner detection datasets are conducted for evaluating its performances. And the experimental results demonstrate that such simple idea endows RJ an incredible improvement and MSRJ achieves the competitive performance compared with state-of-the-arts corner detectors under measure metrics of average repeatability and localization error.
Highlights
Corners are the outstanding local features of image and have played important roles on many applications in image processing and computer vision, such as robot navigation, video retrieval and intelligent transportation systems and so on
In Rosenfeld and Johnston (RJ) algorithm and its improved version, the angles of a digital curve are calculated by K-cosine, where K is the radius of the region of support (RoS)
In this paper, we presented a novel discrete curvature estimator based on the classical RJ algorithm
Summary
Corners are the outstanding local features of image and have played important roles on many applications in image processing and computer vision, such as robot navigation, video retrieval and intelligent transportation systems and so on. CSS-based corner detectors are susceptible to the local variation of the curve and noise generally due to its small neighborhood [30] To overcome the such problems of CSS corner detectors, Awrangjeb and Lu proposed to utilize chord-to-point distance accumulation technique (CPDA) [18] for corner finding and later they presented its accelerated version (fast-CPDA) [19]. To address this issue in CPDA, Teng et al [23] proposed to utilize simple triangular theory and distance calculation for effective and efficient corner detection (CTAR) and Lin et al [28] proposed to use the altitude-to-chord ratio accumulation (ACRA) as the curvature significance.
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