CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative Titlechange detection with structural and textural analysis of images
Thesis Advisor普林特 ; Thomas Corpetti
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword变化检测 马尔霍夫随机场 贝叶斯模型选择 Change Detection Bayesian Model Selection
Abstract变化检测是计算机视觉和图像分析中的一个经典问题。对于同一地点不同时间拍摄的两幅或者多幅图像,变化检测需要给出发生真实变化的区域(变化检测的两类问题),更进一步,我们希望知道发生的变化信息,比如发生的变化类型,时间(变化检测的多类问题)。 变化检测的应用背景十分广泛,视频监控,遥感,医学诊断,城市规划,地下水资源探测,导航等等。一种简单高效的变化检测方法是基于图像灰度的阀值法。阀值法及其延伸的各种方法取得了一些应用成果。但此方法对阀值的选择十分敏感,对检测复杂变换具有十分严重的缺陷。本文以不同时间,不同特性的多幅图像为研究对象,以检测目标级别的变化作为研究目标,结合计算机视觉与模式识别领域的理论和方法,对两类变化和多类变化检测问题进行了理论研究与实验分析。 我们在马尔霍夫随机场的框架下,对两类和多类变化检测进行建模,并采用最大流-最小割方法对马尔霍夫随机场的能量函数最小化。由于输入的图像的不同特性(相机参数,类型,拍摄角度)以及变化种类信息的繁复。我们提出了基于多种相似度测度方法,以增强方法的鲁棒性。这些相似度测度方法都是基于大小可变的图像块计算。另外,我们引入了贝叶斯模型选择和metropolis-hasting采样来处理多类变化问题,试图不仅给出变化区域,更进一步给出变化的语义信息。我们在各种真实图像及合成图像做了大量实验,以定性定量分析验证方法。
Other AbstractAmong the challenging problems in computer vision and image processing, change detection plays an important rule. From two images of possibly different nature, the problem consists in identifying the areas where a real change has appeared (binary label change detection) and discriminating the nature of changed area (multi-label change detection). Applications of change detection techniques are numerous. A traditional way to face this problem relies on a thresholding of the image luminance differences. Such natural approach is efficient but is nevertheless strongly related to the value of the considered threshold and appears limited for textured objects or structures with high internal variability. This thesis is concerned with real change detection in pair of images. This is a challenging and open problem since the difficulties stemming from the confusion between real changes (depending on the objects/structures inside the images) and visual changes (observed through the difference in terms of image luminance) are numerous. Many applications are concerned with this crucial task, like video surveillance, event detection or remote sensing. We propose to solve this labeling problem as the minimization of a global cost-function using a min-cut/max-flow strategy. Because of the different nature of the input images (different sensor, shooting angle, ...) and of the variety of detailed information contained in an object, we propose to rely on several criteria, either able to detect abrupt or subtle changes. These criteria are computed on local patches whose size adaptively depends on the structure of the objects inside the images. In addition, bayesian model selection and metropolis-hasting sampling are introduced to efficiently deal with multi-label change detection. Experimental quantitative and qualitative results are shown on synthetic and real data in various applications.
Other Identifier200828014628033
Document Type学位论文
Recommended Citation
GB/T 7714
龚星. 基于结构和纹理特征的变化检测[D]. 中国科学院自动化研究所. 中国科学院研究生院,2011.
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