NDLI: Optimal Geometric Fitting under the Truncated L2-Norm

Content Provider	IEEE Xplore Digital Library
Author	Ask, E. Enqvist, O. Kahl, F.
Copyright Year	2013
Description	Author affiliation: Centre for Math. Sci., Lund Univ., Lund, Sweden (Ask, E.; Enqvist, O.; Kahl, F.)
Abstract	This paper is concerned with model fitting in the presence of noise and outliers. Previously it has been shown that the number of outliers can be minimized with polynomial complexity in the number of measurements. This paper improves on these results in two ways. First, it is shown that for a large class of problems, the statistically more desirable truncated $L_{2}-norm$ can be optimized with the same complexity. Then, with the same methodology, it is shown how to transform multi-model fitting into a purely combinatorial problem-with worst-case complexity that is polynomial in the number of measurements, though exponential in the number of models. We apply our framework to a series of hard registration and stitching problems demonstrating that the approach is not only of theoretical interest. It gives a practical method for simultaneously dealing with measurement noise and large amounts of outliers for fitting problems with low-dimensional models.
Starting Page	1722
Ending Page	1729
File Size	1198192
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.225
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Computational modeling Complexity theory Noise Fitting Polynomials Truncated Norms Optimization Registration Stitching
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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