Title :
Pseudoconvex Proximal Splitting for L-infinity Problems in Multiview Geometry
Author :
Eriksson, Anders ; Isaksson, Magnus
Author_Institution :
Sch. of Comput. Sci., Univ. of Adelaide, Adelaide, SA, Australia
Abstract :
In this paper we study optimization methods for minimizing large-scale pseudoconvex L∞ problems in multiview geometry. We present a novel algorithm for solving this class of problem based on proximal splitting methods. We provide a brief derivation of the proposed method along with a general convergence analysis. The resulting meta-algorithm requires very little effort in terms of implementation and instead makes use of existing advanced solvers for non-linear optimization. Preliminary experiments on a number of real image datasets indicate that the proposed method experimentally matches or outperforms current state-of-the-art solvers for this class of problems.
Keywords :
convergence of numerical methods; image processing; optimisation; general convergence analysis; large-scale pseudoconvex L∞ problems; meta-algorithm; multiview geometry; nonlinear optimization; optimization methods; pseudoconvex proximal splitting; real image datasets; Algorithm design and analysis; Approximation algorithms; Cameras; Convergence; Geometry; Minimization; Optimization;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
Conference_Location :
Columbus, OH
DOI :
10.1109/CVPR.2014.518