Snakes, shapes, and gradient vector flow

被引:2921
作者
Xu, CY [1 ]
Prince, JL [1 ]
机构
[1] Johns Hopkins Univ, Dept Elect & Comp Engn, Image Anal & Commun Lab, Baltimore, MD 21218 USA
基金
美国国家科学基金会;
关键词
active contour models; deformable surface models; edge detection; gradient vector flow; image segmentation; shape representation and recovery; snakes;
D O I
10.1109/83.661186
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries, problems associated with initialization and poor convergence to boundary concavities, however, have limited their utility, This paper presents a new external force for active contours, largely solving both problems. This external forte, which we call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. It differs fundamentally from traditional snake external forces in that it cannot be written as the negative gradient of a potential function, and the corresponding snake is formulated directly from a force balance condition rather than a variational formulation. Using several two-dimensional (2-D) examples and one three-dimensional (3-D) example, we show that GVF has a large capture range and is able to move snakes into boundary concavities.
引用
收藏
页码:359 / 369
页数:11
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