EAGER: Solving Markov Random Fields with Mutual Exclusion Constraints

本项目探索了一种新的方法来提高马尔科夫随机场（MRF）的表达能力，同时提高计算效率和解决方案的质量。研究的重点是利用以二次等式形式表示的二次互斥约束。目前提高磁磁共振成像表达能力的方法面临着一个非常具有挑战性的问题，即计算时间越长。相比之下，这种方法能够限制qmc的解决方案搜索空间，这反过来不仅可以产生更好的解决方案，还可以减少计算时间。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，都被制定为涉及马尔可夫随机场（MRF）的最大后验（MAP）解推理的优化问题。qmc比用线性等式形式表示的互斥锁约束更通用。因此，qmc提供了更强的表达能力，可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场电位不可靠且不提供信息时，这一特性尤为重要，这在实际应用中是规则而不是例外。因此，本项目可以增加计算机视觉系统的能力，拓宽其应用范围，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.