Dynamic Directional Convolution Vector Field for Active Contour Models

被引：0
|
作者：
Wang, Gang [1 ]
Liang, Jianming [1 ]
Wang, Yang [2 ]
机构：
[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
[2] Shenyang Polytech Coll, Shenyang 110045, Liaoning, Peoples R China
来源：
2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL II | 2010年
基金：
中国博士后科学基金;
关键词：
Active contour models; Dynamic directional; convolution vector field; Gradient vector flow; SEGMENTATION; SNAKES; FLOW;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose a novel dynamic external force for snakes named dynamic directional convolution vector field (DDCVF). It makes use of the gradients of gray-level images and defines positive and negative boundaries in horizontal and vertical directions, respectively. Furthermore, DDCVF is calculated by convolving the user-defined vector field kernel with the edge map generated from the image in the two directions separately. Experimental results show that the DDCVF snake has a large capture range and better robustness to disturbance and initialization.
引用
收藏
页码：43 / 46
页数：4
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