EAGER: Solving Markov Random Fields with Mutual Exclusion Constraints

该项目探索了一种新的方法来增加马尔可夫随机场(MRF)的表达能力，同时提高计算效率和解的质量。研究重点是利用二次等式形式表示的二次互斥约束(QMC)。目前增加MRF表达能力的方法面临着更高的计算时间的非常具有挑战性的问题。计算机视觉中的许多问题，包括但不限于图像和视频分割、立体和图像恢复、目标检测和识别、跟踪和活动识别，都被描述为涉及马尔可夫随机场(MRF)的最大后验概率(MAP)解的优化问题。QMC比以线性相等形式表示的互斥约束更通用。因此，QMC提供了更强的表达能力，以更准确地模拟许多计算机视觉问题。当一元和二元磁流变流变势不可靠且不能提供信息时，这一性质尤其重要，这是实际应用中的规则，而不是例外。因此，该项目可以提高计算机视觉系统的能力，扩大其应用范围，从图像检索到移动机器人上的计算机视觉系统。

This project explores a new way to increase the expressive power of Markov Random Fields (MRF) while at the same time improving the computing efficiency and the quality of solutions. The research focuses on utilizing quadratic mutual exclusion constraints (QMCs) expressed in quadratic equality form. Current approaches to increasing the expressive power of MRFs face a very challenging problem of higher computing time. In contrast, this approach is able to restrict the solution search space with QMCs, which in turn not only leads to significantly better solutions but also to reduced computing time.Many problems in computer vision, including but not limited to image and video segmentation, stereo, and image restoration, object detection and recognition, tracking, and activity recognition, are formulated as optimization problems involving inference of the maximum a posteriori (MAP) solution of a Markov Random Field (MRF). QMCs are more general than mutex constraints expressed in a linear equality form. Hence QMCs offer increased expressive power to more accurately model many computer vision problems. This property is particularly important when unary and binary MRF potentials are unreliable and uninformative, which is the rule rather than an exception in real applications. Hence, this project can increase the ability of computer vision systems to broaden their application scope, ranging from image retrieval to computer vision systems on mobile robots.