On the Extraction of Curve Skeletons using Gradient Vector Flow

In this paper, we propose a new variational framework for computing continuous curve skeletons from discrete objects that are suitable for structural shape representation. We have derived a new energy function, which is proportional to some medialness function, such that the minimum cost path betwee...

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Bibliographic Details
Main Authors: Hassouna, M.S., Farag, A.A.
Format: Conference Proceeding
Language:English
Subjects:
Computer vision
Cost function
Euclidean distance
Image processing
Laboratories
Machine intelligence
Noise robustness
Shape control
Skeleton
Structural shapes
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Description
Summary:In this paper, we propose a new variational framework for computing continuous curve skeletons from discrete objects that are suitable for structural shape representation. We have derived a new energy function, which is proportional to some medialness function, such that the minimum cost path between any two medial voxels in the shape is a curve skeleton. We have employed two different medialness functions; the Euclidean distance field and a variant of the magnitude of the gradient vector flow (GVF), resulting in two different energy functions. The first energy controls the identification of the shape topological nodes from which curve skeletons start, while the second one controls the extraction of curve skeletons. The accuracy and robustness of the proposed framework are validated both quantitatively and qualitatively against competing techniques as well as several 3D shapes of different complexity.
ISSN:1550-5499
2380-7504
DOI:10.1109/ICCV.2007.4409112