Abstract
We have developed a unified computational framework for the inference of multiple salient structures such as junctions, curves, regions, and surfaces from any combinations of points, curve elements, surface elements, in 2-D and 3-D. The methodology is grounded in two elements: tensor calculus for representation, and voting for data communication. The proposed methodology is non-iterative, requires no initial guess or thresholding, and can handle the presence of multiple curves, regions, and surfaces in a large amount of noise while still preserves discontinuities, and the only free parameter is scale. We will demonstrate the approach on a number of examples, both in 2-D and 3-D. Our software is available on the WWW to the community for experimentation and evaluation.
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Tang, CK., Lee, MS., Medioni, G. (2000). Tensor Voting. In: Boyer, K.L., Sarkar, S. (eds) Perceptual Organization for Artificial Vision Systems. The Kluwer International Series in Engineering and Computer Science, vol 546. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4413-5_12
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DOI: https://doi.org/10.1007/978-1-4615-4413-5_12
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