电光与控制, 2016, 23 (11): 73, 网络出版: 2016-11-30
一种基于广义似然比的最小方差活动轮廓模型
A Minimum Variance Active Contour Model Based on Generalized Likelihood Ratio
图像分割 活动轮廓模型 特征提取 广义似然比 能量函数 image segmentation active contour model feature extraction generalized likelihood ratio energy function
摘要
针对传统活动轮廓模型(Snake)对初始轮廓要求高、无法进行多目标提取且抗噪性弱等缺陷, 提出了一种新的基于广义似然比的最小方差活动轮廓模型。该算法在区域活动轮廓模型的基础上引入广义似然比信息, 以目标区域和背景区域具有最小方差为准则设计了新的能量函数, 并使用梯度下降法最小化能量函数, 驱动轮廓线不断收缩至物体边界。合成图像和真实图像的实验结果证明, 基于新模型的活动轮廓提取算法对初始位置不敏感, 具有一定的抗噪性, 并适用于多目标场景。
Abstract
The traditional active contour model is sensitive to initial contour and noise, and is unable to extract multiple targets.To solve these problems, a novel minimum variance active contour model is proposed based on generalized likelihood ratio.The generalized likelihood ratio information is introduced to a region-based active contour model.A novel energy function is designed under the criteria of minimum variance between the target area and background area, which is then minimized by using a gradient descent method to drive the contour shrinking to object borders.Experimental results on synthetic and real images prove that the proposed model is not sensitive to the initial contour position or noise, and is adaptable to multi-target scenario.
琚映云, 周鑫, 翟济云. 一种基于广义似然比的最小方差活动轮廓模型[J]. 电光与控制, 2016, 23(11): 73. JU Ying-yun, ZHOU Xin, ZHAI Ji-yun. A Minimum Variance Active Contour Model Based on Generalized Likelihood Ratio[J]. Electronics Optics & Control, 2016, 23(11): 73.