Abstract
In this paper, we introduce a novel geometric voting scheme that extends previous algorithms, like Hough transform and tensor voting, in order to tackle perceptual organization problems. Our approach is grounded in three methodologies: representation of information using Conformal Geometric Algebra, a local voting process, which introduce global perceptual considerations at low level, and a global voting process, which clusters salient geometric entities in the whole image. Since geometric algebra is the mathematical framework of our approach, our algorithm infers high-level geometric representations from tokens that are perceptually salient in an image.
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Altamirano-Gómez, G.E., Bayro-Corrochano, E. (2014). Conformal Geometric Method for Voting. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_97
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DOI: https://doi.org/10.1007/978-3-319-12568-8_97
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12567-1
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