Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Solving quadratically constrained geometrical problems using Lagrangian duality|
|Citation:||Proceedings of the 19th International Conference on Pattern Recognition, 2008;. pp.1-5|
|Conference Name:||International Conference on Pattern Recognition (19th : 2008 : Florida)|
|Carl Olsson & Anders Eriksson|
|Abstract:||In this paper we consider the problem of solving different pose and registration problems under rotational constraints. Traditionally, methods such as the iterative closest point algorithm have been used to solve these problems. They may however get stuck in local minima due to the non-convexity of the problem. In recent years methods for finding the global optimum, based on Branch and Bound and convex under-estimators, have been developed. These methods are provably optimal, however since they are based on global optimization methods they are in general more time consuming than local methods. In this paper we adopt a dual approach. Rather than trying to find the globally optimal solution we investigate the quality of the solutions obtained using Lagrange duality. Our approach allows us to formulate a single convex semidefinite program that approximates the original problem well.|
|Appears in Collections:||Aurora harvest|
Computer Science publications
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.