1. 华中科技大学图像识别与人工智能研究所,多谱信息处理技术国防科技重点实验室,湖北,武汉,430074
2. 华中科技大学生命科学与技术学院,图像信息处理与智能控制教育部重点实验室,湖北,武汉,430074
3. 华中科技大学图像识别与人工智能研究所多谱信息处理技术国防科技重点实验室湖北武汉,430074
4. 华中科技大学生命科学与技术学院图像信息处理与智能控制教育部重点实验室湖北武汉,430074
纸质出版:2009
移动端阅览
孙阳光, 蔡 超, 周成平, 等. R-Snake:一种基于边缘与区域信息的图像主动轮廓提取模型[J]. 电子学报, 2009,37(8):1810-1815.
SUN Yang-guang, CAI Chao, ZHOU Cheng-ping, et al. R-Snake:A Snake Model Using Both Boundary and Region Information[J]. Acta Electronica Sinica, 2009, 37(8): 1810-1815.
传统Snake模型存在着对轮廓的初始化敏感
对高噪声图像易陷入局部极小值
以及对具有狭长深度凹陷区域的图像无法获得正确轮廓等问题.本文提出了一种基于边缘与区域信息的主动轮廓模型R-Snake(Region Snake).该模型通过文中设计的图像变换算子
并结合区域积分与曲线积分间转化的Green公式
导出了包含目标图像区域信息的区域力.然后由力平衡方程将该区域信息自然直接地引入到主动轮廓提取模型中
从而实现图像的轮廓提取.由于该模型同时利用了图像的区域信息和梯度信息来引导轮廓曲线的演化
使得本文方法不仅扩大了轮廓初始化的范围
降低了对图像噪声的敏感性
而且还增加了轮廓曲线收敛到真实边界的能力.实验结果表明
本文方法具有很强的适应性和鲁棒性
尤其是对高噪声图像和具有狭长深度凹陷的图像获得了优于传统Snake模型的结果.
Traditional Snake model is sensitive to the initialization of contour
easily relapsed into a local optimal in a high noise image
and invalid for the image contour with deeply narrow concavities.By designing the image transform operator to derive the region force from the region information included in the interested object
and using Green formula with the conversation ability between region integral and curve integral
a novel snake model R-Snake(Region Snake)was proposed in this paper to extract the contour of interested object
which more directly introduces region information to active contour model in terms of the force balance equation.Because of evolving the contour curve by using both region information and gradient information
our proposed method could not only extend the initialization of contour and alleviate the sensitivity to image noise
but also improve the capacity to converge into complex boundary.Compared with the traditional Snake model
experimental results demonstrated its feasibility and robustness
especially for the images with high noise and deeply narrow concavities.
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