期刊论文

Active Contour Image Segmentation Combined with Statistical Modeling of Distribution Metrics

中文

计算机工程与应用

1002-8331

2020

56

7

228-233

10.3778/j.issn.1002-8331.1910-0323

湖北省教育科学规划2019年度一般课题(No.2019GB140) 武汉学院校级科学研究项目(No.KYZ201908)。

本校为其他机构

图像分割是数字图像处理中不可或缺的关键步骤。为了解决传统主动轮廓模型针对非匀质图像分割结果不准确且分割效率低的问题,提出一种结合分布度量统计建模的主动轮廓图像分割算法。所提算法的能量驱动力兼顾了图像的全局统计建模信息和其他混合灰度分布信息,使得分割曲线能够更加精确地演化至目标边缘。分布度量能量驱动力定义为轮廓内外概率密度函数定义的比率距离的方差,该能量驱动力基于图像全局信息统计建模,能够更加精确地描述轮廓曲线内外的能量变化;混合灰度分布能量驱动力由图像灰度值与融合均值与中值的区域拟合中心的L2范数表示。将分布度量能量驱动力与混合灰度分布能量驱动力组合形...

Image segmentation is an indispensable key step in digital image processing. In order to solve the problem that the traditional active contour model is inaccurate for the non-homogeneous image segmentation and the segmentation efficiency is low, this paper proposes an active contour image segmentation algorithm combined with statistical modeling of distribution metrics. The energy driving force of the proposed algorithm takes into account the global statistical modeling information of the image and other mixed gray distribution information, so that the segmentation curve can be more accurately...