本文为澳大利亚莫纳什大学（作者：Torsten Seemann）的博士论文，共300页。

本文提出了一种实现“局部分割”的低层次图像处理的统一思想。局部分割提供了一种检查并理解现有算法的方式，以及创建新算法的范例。局部分割可以应用于重要的图像处理任务。利用传统的强度阈值分割技术和简单的模型选择准则，与现有算法相比，新的FUELS去噪算法显示出很强的竞争力。为了改进局部分割，采用最小消息长度的信息理论模型选择准则（MML）对不同结构和复杂度的模型进行选择，从而进一步提升了去噪性能。FUELS和MML变体算法不需要特殊的用户提供的参数，而是从图像本身中进行学习。我们相信，图像处理可以从局部分割方法的应用中受益匪浅。

A unifying philosophy for carrying out low level image processingcalled “local segmentation” is presented. Local segmentation provides a way toexamine and understand existing algorithms, as well as a paradigm for creatingnew ones. Local segmentation may be applied to range of important imageprocessing tasks. Using a traditional segmentation technique in intensitythresholding and a simple model selection criterion, the new FUELS denoising algorithmis shown to be highly competitive with state-of-the-art algorithms on a rangeof images. In an effort to improve the local segmentation, the minimum messagelength information theoretic criterion for model selection (MML) is used toselect between models having different structure and complexity. This leads tofurther improvements in denoising performance. Both FUELS and the MML variantsthereof require no special user supplied parameters, but instead learn from theimage itself. It is believed that image processing in general could benefitgreatly from the application of the local segmentation methodology.

1 引言
2 符号与术语
3 图像处理中的局部分割
4 局部分割去噪
5 基于信息理论的局部分割
6 局部分割的扩展与进一步应用
7 结论

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