Abstract
We solve the tasks of strip line detection and thinning in image processing and pattern recognition in help of an energy minimization technique called rival penalized competitive learning (RPCL) based local principal component analysis (PCA). Due to its model selection and noise resistance ability, the technique is shown to outperform conventional Hough transform and thinning algorithms via a number of simulations.
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Liu, ZY., Chiu, KC., Xu, L. (2003). Local PCA for Strip Line Detection and Thinning. In: Rangarajan, A., Figueiredo, M., Zerubia, J. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2003. Lecture Notes in Computer Science, vol 2683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45063-4_2
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DOI: https://doi.org/10.1007/978-3-540-45063-4_2
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