CASIA OpenIR  > 毕业生  > 博士学位论文
图像与视频分割 
其他题名Image and Video Segmentation
杨明辉
学位类型工学博士
导师彭思龙
2006-06-03
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词分水岭分割 隐马尔可夫树模型 全局运动模型 自动运动目标检测 Watershed Segmentation Hidden Markov Tree Model Global Motion Model Automatic Moving Target Detection
摘要图像分割是数字图像处理中的关键技术之一。通过图像分割技术,可以将图像中人们感兴趣的特征部分提取出来,其有意义的特征包括图像中的边缘、区域、运动信息等,这是进一步进行图像识别、分析和理解的基础。本论文针对不同的应用情况,研究不同的图像分割方法对图像进行分割。 在集成电路反向分析时,需要提取彩色图像中的线目标。针对这一应用目的,本论文提出了一种分水岭分割的方向迭代后处理算法。首先对图像进行分水岭分割得到初始过分割图像,并通过模糊聚类方法得到区域分类概率,然后根据图像的边缘信息和空间特性,得到区域的线方向邻接区域,最后通过迭代方法,利用线方向上邻接区域信息更新当前区域的分类概率。 在纹理分割时,通过应用一种局部参数的小波域隐马尔可夫树(HMT)模型,分析不同纹理样本在不同尺度下的小波系数特性,得到不同尺度下的局部参数,通过聚类的方法对此局部参数进行分类,提取出不同纹理样本的HMT参数特征,然后依照不同纹理样本的HMT参数,对纹理图像进行不同尺度的分割,并通过尺度间分割结果的融合,得到最终的纹理分割结果。本方法在获取纹理的HMT参数时无需进行复杂的训练,且参数反映了纹理的局部特性,取得了不错的分割结果。 本论文在视频分割方面,也进行了研究,致力于研究一种快速的硬件可实现的运动分割方法。本论文进行了两方面的尝试,一方面是建立一种简化的三参数平移旋转运动模型,并依据此模型确定视频序列的全局运动,然后依据全局运动模型对图像进行运动补偿,进而采用运动检测的方法得出前景与背景的分割结果;另一方面是自动运动目标检测的方法,将视频图像划分为几个集合,不同集合具有不同的运动模型,反映视频图像中多个运动目标及背景的运动,首先对已有的不同集合进行运动参数估计,再将运动参数进行融合,以确定当前图像的目标个数,然后通过融合后的运动参数对图像进行分割,并对分割结果进行分裂与融合,引入图像的空间信息,使分割结果逼近目标的原始边界。 理论分析与实验结果表明,本论文的算法在创新性、精确度、复杂度、实用性等方面都有着各自的优势,为图像分割及视频分割在不同领域的应用作出了有益的探索。
其他摘要Image segmentation is one of the most important techniques in digital image processing. Through image segmentation, the characteristics of people interested can be extracted from images. Image segmentation is the foundation of further steps to image recognition, analysis and understanding. This dissertation makes researches on different segmentation methods for different applications. In the IC reverse analysis engineering, we need extract the line objects from the color images. For this purpose, this dissertation introduces a watershed-based post-processing algorithm. Original over-segmented image is made by watershed transform and classified probability of each region is obtained using fuzzy clustering method. Then we extract the neighboring regions in the line direction of each region based on edge and spatial characteristic. Finally the classified probability of each region is updated by its neighboring regions. Experimental results show that the segmentation gives very good extraction result. In texture segmentation, through the application of a wavelet-domain Hidden Markov Tree (HMT) model with localized parameters, I analyze the wavelet coefficients in different scales of different texture pattern and get localized parameters in different scales. Then we cluster these localized parameters and extract the HMT parameters features of different texture pattern. Using these features, the texture image is segmented in each scale. After merging the segmentation results in different scale, we can get the finally segmentation results. Parameters of the HMT model are determined without complex training process and reflect the local character of texture. This makes the segmentation results good. This dissertation is also studied in the aspect of video segmentation. I try to research a kind of fast motion segmentation algorithm which can be realized by hardware. Two kinds of algorithms are tested. On the one hand, a simplified translation and rotation model with 3 parameters is established and the global motion of each video frame is estimated. After global motion compensation, the foreground and background can be separated by motion detection. On the other hand, a motion segmentation algorithm with automatic moving target detection is given. The video frame is divided into several sets, different set have different motion model, which reflect the objects and background in video frame. Firstly, motion parameter estimation is done for each set. Secondly, these parameters are merged to decide how much objects in this video frame. Thirdly, segmentation of this frame is done using these parameters. Finally, segmentation result is refined using spatial information. Theory analysis and experimental results show that the algorithms proposed in this dissertation have respective advantages, such as innovation, accuracy, simplicity, applicability, etc. This dissertation explores a reasonable way in the application domain of image and video segmentation.
馆藏号XWLW996
其他标识符200318014603037
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5931
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
杨明辉. 图像与视频分割 [D]. 中国科学院自动化研究所. 中国科学院研究生院,2006.
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