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基于自适应窗口固定及传播的多尺度纹理图像分割

ISSN号：0732-2112
期刊名称：电子学报
时间：0
页码：5-5
语言：中文
分类：TN911.73[电子电信—通信与信息系统;电子电信—信息与通信工程]
作者机构：[1]西安电子科技大学智能信息处理研究所和智能感知与图像理解教育部重点实验室,陕西西安710071
相关基金：国家自然科学基金（No.60672126,No.60673097）;国家863高技术研究发展计划（No.2007AA12Z136）;国家973重点基础研究发展规划（No.2006CB705707）
相关项目：基于Directionlet的图像方向信息检测

关键词：多尺度纹理图像分割, 马尔科夫性, 小波域隐马尔科夫树模型(WHMT), 多尺度窗口固定传播, 位置信息, 多项式展开和置信区间交叉(LPA-ICI), 融合, multicale texture image segmentation, Markov, Wavelet domain Hidden Markov Tree model（WHMT）, adaptive window fixing and propagation, Local polynomial approximation and Intersection of Confidence Intervals（LPA_ ICI）, fusion

中文摘要：

本文提出一种基于自适应窗口固定及传播的多尺度纹理图像分割方法,在小波域隐马尔科夫树模型（WHMT）的初始分割基础上,根据分割粗尺度上的区域一致性好,细尺度上的边缘准确的特点,利用上下尺度像素之间以及本层邻域像素的马尔科夫性,标记出图像的一致性区域和边缘区域,将一致性区域固定,类标直接下传到下一尺度,边缘区域则利用邻域信息确定出上文权值背景传播到下一尺度,与下尺度一起共同指导图像分割,从而很好的保持了区域均匀性和边缘准确性.同时根据纹理图像区域聚集性的特性,利用基于多项式展开和置信区间交叉（LPA-ICI）方法找出各类区域聚集的物理位置中心,融入上下文权值背景中,使得指导分割策略能够更好的进行.实验表明,对于合成纹理图像来说,本文提出的多尺度融合算法在均匀区域内部及区域边界都大为改善,而且无须进行参数的训练,使算法快速的完成.

英文摘要：

In this paper, we present a multiscale texture image segmentation algorithm based on adaptive window fixing and propagation. After raw segmentation based on wavelet domain hidden markov tree model, we use the fine consistency in coarse scale and accurate edges in fine scale of the raw segmentation result,label the homogeneity region and discontinuity region with different markers according to the characteristics of Markov between the node and its neighborhood and its father node, then we fix the homogeneity region and transfer their labels into their childrens; To discontinuity region, we determine a weight context by the neighbor information and transfer into the next scale to instruct the segmentation with the information of this scale, finally we maintain the uniformity of regions and accuracy of edges. At the same lime, we use an algorithm based on the Local polynomial approximation and Intersection of Confidence Intervals（LPA_ ICI） to find out the physical location of centers of each texture due to the feature of aggregation of textures,then integrate them into the weight context so as to lead better segmentation. The experimental results shown that both on segmentation accuracy and boundary localization are greatly improved to synthetic texture images, and the proposed method is fast and does not need training.

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