计算机工程与应用Issue(13)：133-137,244,6.DOI:10.3778/j.issn.1002-8331.1301-0256
基于邻域场拉普拉斯混合模型图像分割的研究
Laplacian mixture model with neighborhood field for image segmentation
摘要
Abstract
Finite mixture model under Laplacian distribution is proposed in order to solve the problem that image segmentation based on Gaussian Mixture Model(GMM)failing to settle tailing situation with heavy-tailed noise, besides, unlike the standard Laplacian Mixture model(LMM)where pixels themselves are considered independent of each other, the proposed method incor-porates the spatial neighborhood relationship of pixels into the standard LMM. In order to estimate model parameters from obser-vations and instead of utilizing an expectation-maximization algorithm, the gradient method is adopted. The experimental results demonstrate the robustness, accuracy, and effectiveness of the method in comparison with the standard LMM and GMM.关键词
拉普拉斯混合模型(LMM)/图像分割/重尾噪声/空间邻域关系Key words
Laplacian Mixture Model(LMM)/image segmentation/heavy-tailed noise/spatial neighborhood relationship分类
信息技术与安全科学引用本文复制引用
罗雷,王士同..基于邻域场拉普拉斯混合模型图像分割的研究[J].计算机工程与应用,2013,(13):133-137,244,6.基金项目
国家自然科学基金（No.61272210）。 （）
