Automatic Image Segmentation with Anisotropic Fast Marching Algorithm and Geodesic Voting
Ghorpade, Vijaya; Cohen, Laurent D. (2015), Automatic Image Segmentation with Anisotropic Fast Marching Algorithm and Geodesic Voting, 2015 IEEE International Conference on Image Processing (ICIP), IEEE : Piscataway, NJ. 10.1109/ICIP.2015.7351355
TypeCommunication / Conférence
Book title2015 IEEE International Conference on Image Processing (ICIP); ICIP 2015
MetadataShow full item record
Abstract (EN)Segmentation methods based on energy minimization techniques like geodesic active contour model generally needs manual intervention to provide initial points to calculate minimal paths. In this paper, we propose complete automation of segmentation. Seeds and Tips are automatically detected, and geodesics are calculated using Anisotropic Fast Marching algorithm. Fast Marching algorithm computes in a single pass, the evolution of the front, at a speed locally given by its position. Anisotropic Fast Marching (AFM) is a variant of Fast Marching, in which the the measure of path length (and the front speed) depends not only on the path position, but also on path direction and orientation. In this work, a gradient based metric has been defined and AFM is evaluated iteratively over a set of points which are automatically detected on the object boundary. Geodesic voting is then applied to get the segmented structure.
Subjects / KeywordsRiemannian metric; Segmentation; Anisotropic Fast Marching algorithm; Geodesic voting
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