Abstract
Corners are the key feature of image. Stable corners are particularly important in the industrial pipelining of beer cap surface defects detection, greatly affecting the efficiency of image matching and detection precision. To find a stable algorithm for the cap surface defects detection, Stable Corner and Stable Ration are proposed to evaluate the stability of corner detectors, which are able to give an intuitive and unified stability description of various corner detection algorithm. After comparing the stability with Difference of Gaussian (DOG) and Features from Accelerated Segment Test (FAST), Harris is selected as the detector of cap surface images due to its high stability. To eliminate the redundant corners detected by Harris, Circular Mask and Harris (CMH) corner detection is proposed. In CMH, a circular mask with an adaptive threshold is adopted to remove the redundant corners, whereby comparing the intensity between the center pixel and others on the mask in a rapid way, more stable corners are obtained eventually. The effectiveness and robustness of CMH are verified in this paper, and the Stable Ratio increased by 16.7% relatively.
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This work is supported by Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 14JC1402200.
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Wang, L., Fei, M., Yang, T. (2017). Circular Mask and Harris Corner Detection on Rotated Images. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_52
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DOI: https://doi.org/10.1007/978-981-10-6370-1_52
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