图像分割利用活动轮廓与图像结构自适应梯度矢量流外力

IF 1.3 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Dong Wang, Xing Dang, Weijing Liu, Yuanquan Wang
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引用次数: 0

摘要

梯度矢量流(GVF)已被证明是一种有效的运动轮廓的外力。但其平滑性约束没有考虑图像结构,使得梯度矢量流场扩散是各向同性的,不能很好地保留弱边缘。方法针对活动轮廓,提出了一种图像结构自适应梯度矢量流(ISAGVF)外力。在该ISAGVF模型中,首先将平滑性约束重新表述为矩阵形式,然后引入图像结构张量。由于结构张量较好地表征了图像结构,因此所提出的ISAGVF模型能够自适应图像结构,ISAGVF蛇形具有较好的弱边缘保存和深凹性收敛性,同时具有GVF蛇形的一些优良特性,如较大的捕获范围和对初始化的不敏感。结果ISAGVF蛇在合成图像和真实图像上的实验证明了这些特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image segmentation using active contours with image structure adaptive gradient vector flow external force
Introduction Gradient vector flow (GVF) has been proven as an effective external force for active contours. However, its smoothness constraint does not take the image structure into account, such that the GVF diffusion is isotropic and cannot preserve weak edges well. Methods In this article, an image structure adaptive gradient vector flow (ISAGVF) external force is proposed for active contours. In the proposed ISAGVF model, the smoothness constraint is first reformulated in matrix form, and then the image structure tensor is incorporated. As the structure tensor characterizes the image structure well, the proposed ISAGVF model can be adaptive to image structure, and the ISAGVF snake performs well on weak edge preservation and deep concavity convergence while possessing some other desirable properties of the GVF snake, such as enlarged capture range and insensitivity to initialization. Results Experiments on synthetic and real images manifest these properties of the ISAGVF snake.
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来源期刊
Frontiers in Applied Mathematics and Statistics
Frontiers in Applied Mathematics and Statistics Mathematics-Statistics and Probability
CiteScore
1.90
自引率
7.10%
发文量
117
审稿时长
14 weeks
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