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二元能量的伪界优化

Pseudo-Bound Optimization for Binary Energies
课程网址: http://videolectures.net/eccv2014_tang_binary_energies/  
主讲教师: Meng Tang
开课单位: 西安大略大学
开课时间: 2014-10-29
课程语种: 英语
中文简介:

高阶和非子模量成对能量对于图像分割,表面匹配,反卷积,跟踪和其他计算机视觉问题很重要。这种能量的最小化通常是NP困难的。一种标准的近似方法是在整个解决方案空间中优化辅助能量,即原始能量的上限。此界限必须适合快速的全局求解器。理想情况下,它也应该与原始功能非常接近,但是在实践中很难找到这样的上限。我们的主要思想是放宽辅助函数的上限条件,并用一系列伪边界代替它,它可以更好地近似原始能量。我们使用快速多项式参数maxflow方法来探索子模伪边界族的所有全局最小值。最好的解决方案是保证减少原始能量,因为该系列至少包括一个辅助功能。我们的伪边界切割算法在许多应用中改善了现有技术:外观熵最小化,目标分布匹配,曲率正则化,图像去卷积和交互式分割。

课程简介: High-order and non-submodular pairwise energies are important for image segmentation, surface matching, deconvolution, tracking and other computer vision problems. Minimization of such energies is generally NP-hard. One standard approximation approach is to optimize an auxiliary function - an upper bound of the original energy across the entire solution space. This bound must be amenable to fast global solvers. Ideally, it should also closely approximate the original functional, but it is very difficult to find such upper bounds in practice. Our main idea is to relax the upper-bound condition for an auxiliary function and to replace it with a family of pseudo-bounds, which can better approximate the original energy. We use fast polynomial parametric maxflow approach to explore all global minima for our family of submodular pseudo-bounds. The best solution is guaranteed to decrease the original energy because the family includes at least one auxiliary function. Our Pseudo-Bound Cuts algorithm improves the state-of-the-art in many applications: appearance entropy minimization, target distribution matching, curvature regularization, image deconvolution and interactive segmentation.
关 键 词: 图像分割; 算法改善
课程来源: 视频讲座网
数据采集: 2020-12-28:zyk
最后编审: 2020-12-28:zyk
阅读次数: 31