RI: Medium: Collaborative Research: Graph Cut Algorithms for Domain-specific Higher Order Priors

RI：中：协作研究：特定领域高阶先验的图割算法

• 批准号: 1161860
• 负责人: Ramin Zabih
• 金额: $ 41.55万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1161860&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Graph

Optimization is a powerful paradigm for expressing and solving a variety of imaging problems. Modern optimization methods have had considerable success on problems that involve interactions between pairs of pixels. This has lead to important advances, but many imaging problems clearly require explicit modeling of higher-order interactions. This project is addressing this challenge through a close collaboration between researchers with expertise in graph algorithms and computer vision. The project is focused on two core applications: MRI image reconstruction and boundary detection in natural images. Besides their innate interest, these applications are closely related to other important imaging problems such as fMRI distortion correction, super-resolution, angiography and road detection.Optimization problems with high-order interactions are inherently difficult from a computational point of view. The computational complexity can be reduced for problems with specific properties. By identifying common properties in many important imaging problems it is possible to design powerful optimization methods that are broadly applicable. The project is bringing together researchers in computer vision and algorithms. The collaboration is leading to new algorithms that are of broad interest to the computer vision and imaging communities. These algorithms have the potential to transform the way that several important classes of problems are solved. All of the algorithms being developed are being carefully evaluated, with their implementations made widely available on a web repository. Dissemination of the ideas is facilitated by workshops and mini-courses being organized at Brown, Cornell and Rutgers.

优化是表达和解决各种成像问题的强大范例。 现代优化方法在涉及像素对之间相互作用的问题上取得了相当大的成功。 这带来了重要的进步，但许多成像问题显然需要对高阶相互作用进行显式建模。 该项目正在通过具有图算法和计算机视觉专业知识的研究人员之间的密切合作来应对这一挑战。该项目专注于两个核心应用：MRI 图像重建和自然图像中的边界检测。 除了它们固有的兴趣之外，这些应用还与其他重要的成像问题密切相关，例如功能磁共振成像畸变校正、超分辨率、血管造影和道路检测。从计算的角度来看，具有高阶相互作用的优化问题本质上是困难的。 对于具有特定属性的问题，可以降低计算复杂度。 通过识别许多重要成像问题的共同属性，可以设计出广泛适用的强大优化方法。该项目汇集了计算机视觉和算法领域的研究人员。 此次合作催生了计算机视觉和成像社区广泛感兴趣的新算法。 这些算法有可能改变几类重要问题的解决方式。 所有正在开发的算法都经过仔细评估，其实现在网络存储库上广泛提供。 布朗大学、康奈尔大学和罗格斯大学举办的研讨会和迷你课程促进了这些想法的传播。