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基于统计推断的图象分割中若干问题的研究
Alternative TitleResearch on Some Issues of Statistics-based Image Segmentation
张永斌
Subtype工学硕士
Thesis Advisor马颂德
2001-06-01
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图像分割
Abstract图象分割是视觉计算研究中最困难的问题,其难点在于问题本身的不确定 性。这就导致了在不同应用背景,不同的物理约束下的众多方法。针对这些方 法,我们必须有一个合理的分类框架,有了这个框架,我们才能从众多的方法 中抓住它们的本质,看到它们存在的困难,提出有效的解决方法,从而推动视 觉计算理论与应用的发展。 基于上述考虑,本文主要的研究内容包括: ◆通过分析视觉计算的理论成果,提出一个新的在视觉理论下的对现有 的图象分割方法的分类框架,引入了数据驱动与模型驱动的分类概念。 我们总结出在能量函数优化的统一框架下,正则化理论与基于bayes的 统计推断方法是异曲同工的,但统计推断方法在描述不确定性上更具 普遍性,正则化理论可以看作统计推断理论的特例。 ◆讨论了MRF—MAP框架下图象分割的模型;引入了统计物理学的最大熵 的方法,推导出了Gibbs的联合概率分布的必然性,更进一步验证了 视觉算法在能量优化上的统一性,使这个理论更为完善。 ◆提出用多分辩率表示来解决MRF—MAP分割的困难的思路。深入研究 了图象多分辩率的金字塔表示方法,从统计理论上定量的证明了在金字 塔模型下可以有效的消除数据的病态,使参数估计问题变的容易,节省 计算量;并且我们用能量优化的方法推导出了金字塔表示的统一方式, 引出了基于非线性滤波的金字塔的概念。 ◆在多分辩率MRF-MAP下深入讨论了参数估计的具体算法,提出用信 息理论中的MDL原理与EM算法结合来有效的估计参数与确定类别 的数目。提出了一个称为自适应边缘传播(Adaptive Edge Propagation) 的优化分割策略,可以很好克服以前的算法中存在本质问题,真正体 现了多分辨率分割的优越性。最后用实验结果全方位的比较了我们提 出的分割框架下的算法与以前的算法,验证了我们的理论的优越性。
Other AbstractImage segmentation is the most difficult problem in visual computation, whose difficulties lie in its intrinsic uncertainties. This lead to many corresponding approaches based on different applications and physical constraints. It is urgent that we should have a sound framework in analyzing all these algorithms, where we are expected to catch the key issues of them, put forward efficient solutions and, moreover, facilitate our current visual computation both in theories and applications. Based the consideration mentioned above, the main issues we have tried to tackled contains: ◆ In the shadows of the fruits of visual computation theories, We proposed a new framework analyzing the current segmentation algorithms, where we first introduced the concept of "data-driven" and "model-driven". We deem that regularazation theories and bayes-based statistical inference are intrinsically same in the sense of the optimization of energy function when tackling ill-posed problems but the latter one has a wider meaning when come to describing uncertainties in visual computations. ◆ We discussed the segmentation models based on MRF-MAP framework; and we deduced Gibbs probability distribution through maximum entropy principle in statistical physics, which verified the unified visual computation theories in the sense of the optimization of energy function. ◆ We proposed the multi-resolution representation of image to tackle the difficulties of segmentation. In additions, we dig into the pyramids theories and proved qualitatively that it is expected to get good parameters estimation results using pyramid representation; And We got the unified way to generate pyramid representations under the framework of the optimizations of energy function and introduced a promising idea called nonlinear pyramid. ◆ We dig deeply into the algorithms of parameter estimation and proposed an algorithm integrating EM and MDL to get the parameters and the number of regions intended. We also introduced a novel algorithm called Adaptive Edge Propagation to optimizing the MRF-MAP function, Which can deal with the inherent problems in former algorithms proposed and get satisfactory results. Comparative experiments results to others algorithms showed the superiority of our theory.
shelfnumXWLW609
Other Identifier609
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6774
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
张永斌. 基于统计推断的图象分割中若干问题的研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2001.
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