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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.

Robust Corner Finding Based on Multi-Scale K-Cosine Angle Detection