Corner is widely utilized in computer vision and image processing. As a representative contourbased 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. INDEX TERMS Corner detection, image processing, multi-scale product, average repeatability. I. INTRODUCTION 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. By now, plenty of corner detectors have been proposed, which can be broadly separated into two kinds: intensity-based detectors [1]-[9] and contour-based detectors [11]-[29]. With respect to contour-based methods, corners are mainly detected on the boundaries extracted from images among which estimating the digital curvature of the boundaries is the critical step. Rosenfeld and Johnston (RJ) [11] first considered angle as the discrete curvature to find corners for digital curves and later they presented its improved version which used average angle instead [12]. In 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). Since the angle The associate editor coordinating the review of this manuscript and approving it for publication was Zhihan Lv .