Combinatorial Optimization Methods for Computer Vision and Bio-medical Image Analysis

计算机视觉和生物医学图像分析的组合优化方法

• 批准号: 298299-2012
• 负责人: Boykov, Yuri
• 金额: $ 3.06万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=571468
• 关键词: Combinatorial; Optimization; Methods; Computer; Vision

Automatic computer/robot vision for manufacturing, health care, security, and multi-media is still largely "work in progress" and relatively few methods produce consistently reliable results on real data. Sheer size of high-resolution volumetric images in stereo-vision, motion analysis, computer-assisted diagnosis (e.g. MRI, CT), and other applications demands very high level of efficiency from computer vision algorithms. Despite significant progress in the last 10-20 years, the vision community is still widely researching new theories and mathematical concepts that could lead to computationally feasible methods for image segmentation, shape representation, model fitting, stereo, and many other low-level problems forming the base for all computer vision systems. My research concentrates on practical computationally-efficient and mathematically solid models for low-level vision. This topic offers an exciting ground for creative interdisciplinary research linking optimization, statistical physics, information theory, learning, applied differential geometry, and other mathematical sciences. Most low-level vision problems can be formulated as optimization problems using either information-based methodology (e.g. minimum description length principle), or concepts from statistical physics (e.g. posterior energy), or differential geometry (minimum surface). The focus of my proposed research are mathematically solid models in low-level vision and the corresponding fast optimization methods computing either their global minimum or some guaranteed-quality approximation. Such optimization problems are a challenge for the state of the art in discrete combinatorial algorithms and continuous variational techniques. Firstly, it was shown that many problems in vision are intrinsically difficult (NP-hard), thus effective approximations must be explored. Secondly, computational efficiency and scalability of the proposed optimization algorithms is crucial when a solution is sought on huge 3D or 4D image volumes common in vision and medical imaging.

用于制造业、医疗保健、安全和多媒体的自动计算机/机器人视觉仍然在很大程度上 “正在进行的工作”和相对较少的方法在真实的数据上产生一致可靠的结果。立体视觉、运动分析、计算机辅助诊断（例如MRI、CT）和其他应用中的高分辨率体积图像的纯粹尺寸要求计算机视觉算法具有非常高的效率水平。尽管在过去的10-20年中取得了重大进展，但视觉界仍在广泛研究新的理论和数学概念，这些理论和概念可能会导致图像分割，形状表示，模型拟合，立体声和许多其他低级问题的计算可行方法，这些问题构成了所有计算机视觉系统的基础。我的研究集中在实用的计算效率和数学上的低层次视觉模型。本主题为创造性的跨学科研究提供了一个令人兴奋的基础，将优化，统计物理，信息论，学习，应用微分几何和其他数学科学联系起来。大多数低级视觉问题可以用基于信息的方法（例如最小描述长度原则）或统计物理学（例如后验能量）或微分几何（最小表面）的概念来表示为优化问题。 我建议的研究重点是在低层次的视觉和相应的快速优化方法计算其全局最小值或一些高质量的近似数学模型。这样的优化问题是离散组合算法和连续变分技术的最新技术的挑战。首先，它表明，在视觉中的许多问题本质上是困难的（NP-难），因此有效的近似必须探索。其次，当在视觉和医学成像中常见的巨大3D或4D图像体积上寻求解决方案时，所提出的优化算法的计算效率和可扩展性至关重要。