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Abstract: In this paper we address the statistics of second order relations of feature vectors derived from image sequences. We compute the individual vector components corresponding to the visual modalities orientation, contrast transition, optic flow, and color by conventional lowlevel early vision algorithms. As a main result, we observe that collinear (or parallel) line pairs are with very great likelihood also associated with other identical features, for example sharing the same flow pattern, or... (Update)
Context of citations to this paper: More
...explained in the books of Kanizsa, might well be solved by a few information theoretical principles. As a good Kr uger and W org otter [19] gave strong statistical computational evidence in favor of a collaboration between partial gestalt laws, namely collinearity,...
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BibTeX entry: (Update)
N. Kruger and F. Worgotter, \Multi-modal estimation of collinearity and parallelism in natural image sequences", submitted to Network Computation in Neural Systems, 2001. http://citeseer.ist.psu.edu/537705.html More
@misc{ ger-multimodal,
author = "Norbert Kr{\"u}ger and Florentin W{\"o}rg{\"o}tter",
title = "Multi-modal Estimation of Collinearity and Parallelism in Natural Image
Sequences",
url = "citeseer.ist.psu.edu/537705.html" }
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