Multilevel Relaxation in Low-Level Computer Vision
Multilevel Relaxation in Low-Level Computer Vision
复制标题
低级计算机视觉中的多级松弛
DOI：
10.1007/978-3-642-51590-3_18
复制
发表时间：
1984
期刊：
影响因子：
--
通讯作者：
F. Glazer
中科院分区：
文献类型：
--
作者：
F. Glazer
变分（成本最小化）和局部约束方法通常适用于低级视觉中的问题（例如，内在图像的计算）。迭代松弛算法是实现的“自然”选择，因为它们可以在高度并行和本地连接的处理器上执行。然而，它们可能需要非常大量的迭代来达到收敛。多级松弛技术收敛速度更快，非常适合在圆锥或金字塔中处理。这些技术被应用于从动态图像计算光流的问题。
Variational (cost minimization) and local constraint approaches are generally applicable to problems in low-level vision (e.g., computation of intrinsic images). Iterative relaxation algorithms are “natural” choices for implementation because they can be executed on highly parallel and locally connected processors. They may, however, require a very large number of iterations to attain convergence. Multilevel relaxation techniques converge much faster and are well suited to processing in cones or pyramids. These techniques are applied to the problem of computing optic flow from dynamic images.