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Variational Curve Skeletons Using Gradient Vector Flow
December 2009 (vol. 31 no. 12)
pp. 2257-2274
M. Sabry Hassouna, Vital Images, Inc., Eden Prairie
Aly A. Farag, University of Louisville, Louisville
Representing a 3D shape by a set of 1D 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, subvoxel 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 these 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 hence are less sensitive to noise.

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Index Terms:
Curve skeletons, shape representation, skeletonization, gradient vector flow, Eikonal equation, centerline extraction, path planning, medial axis.
Citation:
M. Sabry Hassouna, Aly A. Farag, "Variational Curve Skeletons Using Gradient Vector Flow," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2257-2274, Dec. 2009, doi:10.1109/TPAMI.2008.271
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