An efficient approach for boundary based corner detection by maximizing bending ratio and curvature
This paper introduces a novel corner detection method, which is based on the bending ratio of a moving window along with a local curvature approximation. A pre-processing step is first carried out in order to find one-pixel thin object boundaries. The proposed method traces over these boundaries, as we refer to as sub-segments and as the first step all potential corners are extracted by finding the maximum bending ratio in the moving window. The exact corner position within the window is then located accurately using the pixel-based curvature approximation. A corner factor can then be assigned to a potential corner using the maximum bending ratio and curvature values and among all potential corners, non-maximum suppression is applied to a group in close proximity and thus only the ones with the highest corner factors survive. In this way the spurious corners are significantly reduced, whilst keeping the true corners. A dedicated set of experimental results approve that the proposed method is highly accurate, computationally efficient and robust resolution and scale variations..