DocumentCode :
3549010
Title :
Level set evolution without re-initialization: a new variational formulation
Author :
Li, Chunming ; Xu, Chenyang ; Gui, Changfeng ; Fox, Martin D.
Author_Institution :
Dept. of Electr. & Comput. Eng., Connecticut Univ., Storrs, CT, USA
Volume :
1
fYear :
2005
fDate :
20-25 June 2005
Firstpage :
430
Abstract :
In this paper, we present a new variational formulation for geometric active contours that forces the level set function to be close to a signed distance function, and therefore completely eliminates the need of the costly re-initialization procedure. Our variational formulation consists of an internal energy term that penalizes the deviation of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image features, such as object boundaries. The resulting evolution of the level set function is the gradient flow that minimizes the overall energy functional. The proposed variational level set formulation has three main advantages over the traditional level set formulations. First, a significantly larger time step can be used for numerically solving the evolution partial differential equation, and therefore speeds up the curve evolution. Second, the level set function can be initialized with general functions that are more efficient to construct and easier to use in practice than the widely used signed distance function. Third, the level set evolution in our formulation can be easily implemented by simple finite difference scheme and is computationally more efficient. The proposed algorithm has been applied to both simulated and real images with promising results.
Keywords :
computational geometry; edge detection; finite difference methods; image segmentation; partial differential equations; variational techniques; curve evolution; external energy term; finite difference; geometric active contours; gradient flow; internal energy term; level set evolution; level set function; partial differential equation; signed distance function; variational formulation; Active contours; Computed tomography; Computer vision; Data visualization; Image segmentation; Lagrangian functions; Level set; Mathematics; Partial differential equations; Solid modeling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on
ISSN :
1063-6919
Print_ISBN :
0-7695-2372-2
Type :
conf
DOI :
10.1109/CVPR.2005.213
Filename :
1467299
Link To Document :
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