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Variational image segmentation based on pixel pairwise similarities.

机译：基于像素对相似性的变分图像分割。

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摘要

The main goal of this thesis is to develop robust computational methods to address some of the open problems arising in the field of variational image segmentation. In particular, we focus our attention on a specific sub-class of variational methods, proposing several novel variational frameworks based on pixel pairwise (dis)similarities. The starting point for most of the main contributions can be considered Graph Partitioning Active Contours, the framework for binary (i.e. foreground/background) segmentation based on pixel similarities introduced in [122]. The motivation for the work of this thesis stems from the fact that a general binary segmentation framework is not adequate in the case of natural images, which usually require segmentation into multiple regions. Furthermore, in the presence of occlusion or high levels of noise, prior information about objects of interest or domain knowledge would be needed for a robust segmentation. This research addresses these issues in two main directions.;First, we introduce novel variational frameworks based on pairwise pixel similarities for the segmentation in multiple regions. This can be considered a principled solution to the multi-region segmentation problem, that in previous work on pairwise similarity based cost functions have been solved mainly by recursive bi-partition. In addition, we explicitly address the problem of multiphase curve regularization. In fact, when extending curve evolution frameworks to segment images in multiple regions, traditional regularization techniques, aimed at increasing robustness to noise and artifacts, cease to be adequate. We therefore design novel length and area regularization terms, whose minimization yields evolution equations more suitable to eliminate spurious regions and other kind of noisy artifacts. To the best of our knowledge, this is the first attempt in this direction.;Secondly, we address the problem of introducing prior knowledge within the segmentation framework. Prior information in the form of multiple views of the same object/scene is incorporated by reformulating the cost function in [122] in such a way that pixel pairwise dissimilarities are computed across different views, granting robustness to occlusions and high level of noise. Minimization of these cost functions is carried out, after imposing a warping constraint between the views. By doing this, the need of introducing a specific shape term in the cost function is avoided and, at the same time, the shape prior is exploited in a more complete way, taking into account also intensity or color information of the emerging regions. We also introduce non-rigid registration models (based on thin plate splines) within the level set framework to cope with non-rigid deformation of the object shape, which, to the best of our knowledge, is the first attempt in this direction. We finally demonstrate that this model is effective in segmenting bio-medical images, in which one of the views is represented by a reference image (or atlas) containing information about the structures of interest.
机译：本文的主要目的是开发鲁棒的计算方法，以解决在变分图像分割领域中出现的一些开放性问题。尤其是，我们将注意力集中在变分方法的特定子类上，提出了一些基于像素对（非）相似性的新颖变分框架。大多数主要贡献的起点可以认为是“图形分区活动轮廓”，这是一种基于[122]中引入的像素相似度的二进制（即前景/背景）分割框架。本文工作的动机是基于这样一个事实：在自然图像的情况下，一般的二进制分割框架是不够的，而自然图像通常需要分割成多个区域。此外，在存在遮挡或高水平的噪声的情况下，需要有关感兴趣对象或领域知识的先验信息以进行鲁棒的分割。这项研究从两个主要方向解决了这些问题。首先，我们介绍了基于成对像素相似性的新颖变异框架，用于在多个区域中进行分割。可以认为这是多区域分割问题的原则性解决方案，在先前关于基于成对相似性的成本函数的工作中，主要是通过递归双向分割来解决的。此外，我们明确解决了多相曲线正则化问题。实际上，当将曲线演化框架扩展为在多个区域中分割图像时，旨在提高对噪声和伪像的鲁棒性的传统正则化技术就不再足够了。因此，我们设计了新颖的长度和面积正则化项，其最小化会产生更适合消除杂散区域和其他类型的噪声伪影的演化方程。据我们所知，这是朝这个方向的首次尝试。其次，我们解决了在分割框架内引入先验知识的问题。通过重新格式化[122]中的成本函数，可以合并同一对象/场景的多个视图形式的先验信息，从而跨不同视图计算像素对的相异性，从而增强了遮挡的鲁棒性和高水平的噪声。在视图之间施加扭曲约束之后，将这些成本函数最小化。通过这样做，避免了在成本函数中引入特定形状项的需要，并且同时还考虑了新兴区域的强度或颜色信息，以更完整的方式利用了形状先验。我们还在水平集框架内引入了非刚性配准模型（基于薄板样条线），以应对对象形状的非刚性变形，据我们所知，这是该方向的首次尝试。最后，我们证明了该模型在分割生物医学图像方面是有效的，其中一个视图由包含有关感兴趣结构的信息的参考图像（或图集）表示。

著录项

  • 作者

    Bertelli, Luca.;

  • 作者单位

    University of California, Santa Barbara.;

  • 授予单位 University of California, Santa Barbara.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 192 p.
  • 总页数 192
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

  • 入库时间 2022-08-17 11:38:27

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