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On the Extraction of Curve Skeletons using Gradient Vector Flow

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2 Author(s)
M. Sabry Hassouna ; Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, Louisville, KY 40292. ; Aly A. Farag

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.

Published in:

2007 IEEE 11th International Conference on Computer Vision

Date of Conference:

14-21 Oct. 2007