Multilevel Relaxation in Low-Level Computer Vision

Abstract

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.