面向图像多源属性的协同分割方法研究
发布时间：2018-08-01 09:32
【摘要】：如何在场景复杂的图片中快速得到用户感兴趣的目标,正成为如今计算机视觉和模式识别领域的热点和难点。图像分割作为一种提取目标的有效途径一直以来都受到学者们的广泛关注,也已经取得了较多的研究成果。但是依旧存在较多的难点,比如:图像包含的信息越来越多,图像的特征也越来越丰富,单一的特征已经不能满足如今的技术要求;在分割粒度方面,基于像素级的分割框架往往会导致分割目标的不完整性,而一些基于区域的分割框架则存在细节方面的丢失,并且较为依赖预分割区域的准确性。本文针对图像的多源属性协同分割问题,主要进行了如下几个方面的创新工作:首先,提出了一种有效的纹理建模方式。通过对传统的多尺度结构张量进行精简和非线性滤波处理得到非线性精简多尺度结构张量,并将非线性精简多尺度结构张量与全局变分流结合构成我们所使用的纹理描述子。采用图割框架对该纹理描述子的有效性进行实验,通过与常用纹理特征的实验对比验证了该纹理描述子在纹理描述力上有较好的效果,而且其较低的维度特质也给后续的概率建模带来了效率上的提升。其次,提出了基于虚拟节点的图像多源属性的协同分割框架,并将该框架用于多特征的协同分割。以L*a*b颜色特征和我们所提出的纹理建模方式为例对基于虚拟节点的颜色纹理协同分割方法进行了实验。通过与传统的颜色纹理能量混合模型和单一的特征的分割结果进行对比,说明了该分割框架能够较好的吸纳不同特征分割中的优势部分。最后,提出了基于上下文信息的图像多源属性的协同分割框架,并将该框架用于多粒度的协同分割。以边缘增强的均值漂移算法得到的同质区域粗粒度和原像素细粒度为例对基于上下文信息的粗细粒度协同分割方法进行了实验。通过与基于单一粒度的分割结果进行对比,说明该分割框架能够在细节和目标整体性上面表现良好。本文通过大量的仿真实验验证了本文纹理建模方式、基于虚拟节点的多特征协同分割和基于上下文信息的多粒度协同分割的实效性和可用性,并具有良好的应用前景。
[Abstract]:How to quickly get users interested in the complex scene is becoming a hot and difficult point in the field of computer vision and pattern recognition. As an effective way to extract the target, image segmentation has been widely concerned by scholars and has also taken more research results. There are many difficulties, such as: more and more information is included in the image, and the features of images are becoming more and more rich. The single feature can not meet the technical requirements of today. In the aspect of segmentation granularity, the segmentation framework based on pixel level often leads to the incompleteness of the segmentation target, and some segmentation frameworks based on the region exist in detail. For the problem of multi source attribute synergetic segmentation, this paper focuses on the following aspects: first, an effective texture modeling method is proposed. The nonlinear precision is obtained by the traditional multi-scale structure Zhang Liangjin row simplification and nonlinear filtering. The texture descriptor of the texture descriptor used by the nonlinear simplification of multi scale structure tensor and global variation is introduced. The validity of the texture descriptor is tested by the graph cut frame. The texture descriptor is better than the common texture descriptor. The effect, and its lower dimensional characteristics also brings efficiency to the subsequent probabilistic modeling. Secondly, a collaborative segmentation framework based on the multi source attributes of virtual nodes is proposed, and the framework is used for multi feature synergetic segmentation. The L*a*b color feature and the texture modeling method we put forward are based on virtual nodes. By comparing with the traditional color texture energy mixing model and the single feature segmentation result, it shows that the segmentation framework can well absorb the advantages of different feature segmentation. Finally, the cooperative segmentation of multi source attributes based on context information is proposed. The frame is cut and the framework is used in multi granularity cooperative segmentation. The coarse granularity and fine grain size of the homogeneous region obtained by the edge enhanced mean shift algorithm is used as an example to experiment on the coarse and fine granularity cooperative segmentation method based on context information. By comparing the segmentation results based on the single granularity, the segmentation framework is illustrated. Through a large number of simulation experiments, this paper validates the texture modeling method in this paper, the effectiveness and availability of multi granularity cooperative segmentation based on virtual nodes and multi granularity based on context information, and has a good application prospect.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
本文编号：2157142
[Abstract]:How to quickly get users interested in the complex scene is becoming a hot and difficult point in the field of computer vision and pattern recognition. As an effective way to extract the target, image segmentation has been widely concerned by scholars and has also taken more research results. There are many difficulties, such as: more and more information is included in the image, and the features of images are becoming more and more rich. The single feature can not meet the technical requirements of today. In the aspect of segmentation granularity, the segmentation framework based on pixel level often leads to the incompleteness of the segmentation target, and some segmentation frameworks based on the region exist in detail. For the problem of multi source attribute synergetic segmentation, this paper focuses on the following aspects: first, an effective texture modeling method is proposed. The nonlinear precision is obtained by the traditional multi-scale structure Zhang Liangjin row simplification and nonlinear filtering. The texture descriptor of the texture descriptor used by the nonlinear simplification of multi scale structure tensor and global variation is introduced. The validity of the texture descriptor is tested by the graph cut frame. The texture descriptor is better than the common texture descriptor. The effect, and its lower dimensional characteristics also brings efficiency to the subsequent probabilistic modeling. Secondly, a collaborative segmentation framework based on the multi source attributes of virtual nodes is proposed, and the framework is used for multi feature synergetic segmentation. The L*a*b color feature and the texture modeling method we put forward are based on virtual nodes. By comparing with the traditional color texture energy mixing model and the single feature segmentation result, it shows that the segmentation framework can well absorb the advantages of different feature segmentation. Finally, the cooperative segmentation of multi source attributes based on context information is proposed. The frame is cut and the framework is used in multi granularity cooperative segmentation. The coarse granularity and fine grain size of the homogeneous region obtained by the edge enhanced mean shift algorithm is used as an example to experiment on the coarse and fine granularity cooperative segmentation method based on context information. By comparing the segmentation results based on the single granularity, the segmentation framework is illustrated. Through a large number of simulation experiments, this paper validates the texture modeling method in this paper, the effectiveness and availability of multi granularity cooperative segmentation based on virtual nodes and multi granularity based on context information, and has a good application prospect.
【学位授予单位】：华中科技大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41
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相关期刊论文 前2条
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2 刘丽;匡纲要;;图像纹理特征提取方法综述[J];中国图象图形学报;2009年04期
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