Abstract
In this paper, we describe image feature as parameterized model and formulate feature detection as robust model fitting problem. It can detect global feature easily without parameter transformation, which is needed by Hough Transform methods. We adopt RANSAC paradigm to solve the problem. It is immune to outliers and can deal with image contains multiple features and noisy pixels. In the voting stage of RANSAC, in contrast with previous methods which need distance computation and comparison, we apply Bresenham algorithm to generate pixels in the inlier region of the feature and use the foreground pixels in this region to vote the potential feature. It greatly improves the efficiency and can detect spatially-linked features easily. Experimental results with both synthetic and real images are reported.
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© 2006 Springer-Verlag Berlin Heidelberg
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Chai, D., Peng, Q. (2006). Image Feature Detection as Robust Model Fitting. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_67
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DOI: https://doi.org/10.1007/11612704_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31244-4
Online ISBN: 978-3-540-32432-4
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