Abstract
This paper first analysed the state-of-the-art corner detection algorithms and then proposed a novel corner detection approach based on a maximum point-to-chord distance. The proposed corner detector consists of three steps: First, several curves of original image is extracted using Canny edge detector. Second, a method of maximum point-to-chord distance is used in each curve to get the initial corner points. Third, non-maximum suppression and threshold are used to remove corner points with low curvature and get the final result. Different from the CPDA (chord-to-point distance accumulation) corner detector, our proposed detector neither need to accumulate each distance from a moving chord, nor need to computer the accumulation of each point in a curve, therefore achieves better speed while keeping the good average repeatability and accuracy. Compared with the existing methods, the proposed detector attains better performance on average repeatability and localization error under affine transforms, JPEG compression and Gaussian noise.
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He, Y., Li, Y., Zhang, W. (2018). Robust Image Corner Detection Based on Maximum Point-to-Chord Distance. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_40
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DOI: https://doi.org/10.1007/978-3-030-00563-4_40
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