Globally Optimal Least Squares Solutions for Quasiconvex 1D Vision Problems
Author
Summary, in English
Solutions to non-linear least squares problems play an essential role in structure and motion problems in computer vision. The predominant approach for solving these problems is a Newton like scheme which uses the: hessian of the function to iteratively find a, local solution. Although fast, this strategy inevitably leeds to issues with poor local minima, and missed global minima. In this paper rather than trying to develop all algorithm that is guaranteed to always work, we show that it is often possible to verify that a local solution is in fact; also global. We present a simple test that verifies optimality of a solution using only a few linear programs. We show oil both synthetic and real data that for the vast majority of cases we are able to verify optimality. Further more we show even if the above test fails it is still often possible to verify that the local solution is global with high probability.
Department/s
Publishing year
2009
Language
English
Pages
686-695
Publication/Series
Image Analysis, Proceedings
Volume
5575
Document type
Conference paper
Publisher
Springer
Topic
- Mathematics
Conference name
16th Scandinavian Conference on Image Analysis
Conference date
2009-06-15 - 2009-06-18
Conference place
Oslo, Norway
Status
Published
ISBN/ISSN/Other
- ISSN: 1611-3349
- ISSN: 0302-9743