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mecca.louisville.edu
Representing a 3D shape by a set of one-dimensional curves that are locally
symmetric with respect to its boundary (i.e., curve skeletons) is of importance
in several machine intelligence tasks. This paper presents a fast, automatic, and robust variational framework for computing continuous, sub-voxel accurate curve skeletons from volumetric objects. A reference point inside the object is considered a point source that transmits two wave fronts of different energies. The first front (beta-front) converts the object into a graph, from which the object salient topological nodes are determined. Curve skeletons are tracked from those nodes along the cost field constructed by the second front (alpha-front) until the point source is reached. The accuracy and robustness of the proposed work are validated against competing
techniques as well as a database of 3D objects. Unlike other state-of-the-art techniques, the proposed framework is highly robust because it avoids locating and classifying skeletal junction nodes, employs a new energy that does not form medial surfaces, and finally extracts curve skeletons that correspond to the most prominent parts of the shape, and are hence less sensitive to noise.
M. Sabry Hassouna, Aly A. Farag
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Curve Skeletons of 3D Objects Related Work (2) M. Sabry Hassouna and Aly A. Farag, "On the Extraction of Curve Skeletons using Gradient Vector Flow," Proc. of IEEE International Conference on Computer Vision ICCV, | |||||||||||||||||||||
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