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- W2126801588 abstract "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." @default.
- W2126801588 created "2016-06-24" @default.
- W2126801588 creator A5008507664 @default.
- W2126801588 creator A5077412935 @default.
- W2126801588 date "2009-12-01" @default.
- W2126801588 modified "2023-09-24" @default.
- W2126801588 title "Variational Curve Skeletons Using Gradient Vector Flow" @default.
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- W2126801588 doi "https://doi.org/10.1109/tpami.2008.271" @default.
- W2126801588 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19834145" @default.
- W2126801588 hasPublicationYear "2009" @default.
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