Discrete Optimization Methods for Computer Vision

计算机视觉的离散优化方法

• 批准号: RGPIN-2017-05413
• 负责人: Veksler, Olga
• 金额: $ 2.88万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=667689
• 关键词: Discrete; Optimization; Methods; Computer; Vision

The goal of computer vision is to develop systems that automatically process visual information. The applications are numerous, ranging from the traditional ones such as industrial inspection and robot navigation, to the newer ones such as video conferencing, and special effects for the movie industry.******Markov Random Fields (MRF) and Conditional Random Fields (CRF) are popular probabilistic models for solving challenging labelling problems that are encountered in computer vision. Models that arise require computationally intensive energy minimisation. For many interesting models, the exact minimisation is an NP-hard problem, and only an approximate solution can be found. Thus developing efficient minimisation techniques is essential for obtaining a good solution. ******I plan to develop more effective methods for minimisation of binary non-submodular energies, which is an NP-hard problem. Binary energies are useful for a variety of problems such as image segmentation, shape priors, etc. Furthermore, any mutli-label energy can be converted to a binary energy. Thus binary non-submodular energies form an important class of energies to handle. In our previous work, we explored two approaches based on the trust region and the auxiliary function frameworks. For most applications, trust region works better than the auxiliary function approach, but auxiliary functions approach is faster. I plan to extend the auxiliary functions approach so that it is as fast but more accurate. ******I also plan to develop effective optimisation algorithms for densely connected CRFs. Densely connected CRFs are gaining popularity recently, especially since they can be combined with the recently hugely successful deep convolutional neural networks (CNNs) into one system. I plan to develop unified CNN and densely connected CRF models that use efficient minimisation methods and can be trained jointly. ******Interactive segmentation is a popular tool in medical image processing. Due to uncertainly in the data, and disagreement even among the medical specialists, automatic segmentation is unlikely to overtake user assisted segmentation in popularity. However, it is important to reduce user effort. I plan to develop segmentation tools that require a minimal user assistance in most cases, for example a single click.******I plan to continue research on segmentation with shape priors. Shape priors result in more accurate image segmentations since they rule out impossible shape solutions. In previous work I considered rather simple generic shape priors such as convexity, symmetry, etc. I will develop shape priors that are more shape-specific. ******The proposed research is intended to produce novel optimisation tools and to advance the applicability of optimisation tools for computer vision problems. This, in turn, will lead to an improved performance for the practical problems in computer vision field.**

计算机视觉的目标是开发自动处理视觉信息的系统。应用范围很广，从工业检查和机器人导航等传统应用，到视频会议和电影行业特效等新兴应用。******马尔可夫随机场 (MRF) 和条件随机场 (CRF) 是流行的概率模型，用于解决计算机视觉中遇到的具有挑战性的标签问题。出现的模型需要计算密集型能量最小化。对于许多有趣的模型来说，精确最小化是一个 NP 难问题，只能找到近似解。因此，开发有效的最小化技术对于获得良好的解决方案至关重要。 ******我计划开发更有效的方法来最小化二元非子模能量，这是一个 NP 难题。二元能量可用于多种问题，例如图像分割、形状先验等。此外，任何多标签能量都可以转换为二元能量。 因此，二元非子模能量形成了一类需要处理的重要能量。在我们之前的工作中，我们探索了两种基于信任域和辅助功能框架的方法。对于大多数应用程序，信赖域比辅助函数方法效果更好，但辅助函数方法更快。我计划扩展辅助函数方法，使其速度更快但更准确。 ******我还计划为密集连接的条件随机场开发有效的优化算法。密集连接的 CRF 最近越来越受欢迎，特别是因为它们可以与最近取得巨大成功的深度卷积神经网络 (CNN) 结合到一个系统中。我计划开发统一的 CNN 和密集连接的 CRF 模型，它们使用高效的最小化方法并且可以联合训练。 ******交互式分割是医学图像处理中的流行工具。由于数据的不确定性，甚至医学专家之间也存在分歧，自动分割不太可能超过用户辅助分割的受欢迎程度。然而，减少用户的工作量很重要。我计划开发在大多数情况下需要最少用户帮助的分割工具，例如单击一次。******我计划继续研究形状先验的分割。形状先验可以导致更准确的图像分割，因为它们排除了不可能的形状解决方案。在之前的工作中，我考虑了相当简单的通用形状先验，例如凸性、对称性等。我将开发更特定于形状的形状先验。 ******所提出的研究旨在产生新颖的优化工具并提高优化工具对计算机视觉问题的适用性。反过来，这将提高计算机视觉领域实际问题的性能。**