We present a novel method to detect multimodal regions composed of linear structures and measure the orientations in these regions, i.e. at line X-sings, T-junctions and Y-forks. In such complex regions an orientation detector should unmix the contributions of the unimodal structures. In our approach we first define a (streamline) divergence metric and apply it to our streamline field to detect junctions. After this step we select all streamlines that intersect a circle of radius r around the junction twice, cluster the intersection points and compute the direction per cluster. This yields a multimodal descriptor of the local orientations in the vicinity of the detected junctions. The method is suited for global analysis and has moderate memory requirements. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Faas, F. G. A., & Van Vliet, L. J. (2007). Junction detection and multi-orientation analysis using streamlines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4673 LNCS, pp. 718–725). Springer Verlag. https://doi.org/10.1007/978-3-540-74272-2_89
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