On the minimal problems of low-rank matrix factorization

Jiang, Fangyuan; Oskarsson, Magnus; Åström, Kalle

doi:10.1109/cvpr.2015.7298870

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…4, we used the well-known dinosaur sequence. This sequence contains very little outliers, and the camera geometry has been shown to be well approximated by an affine model, see, e.g., [11].…”

Section: Semisynthetic Datamentioning

confidence: 99%

Two-View Orthographic Epipolar Geometry: Minimal and Optimal Solvers

Oskarsson

2017

J Math Imaging Vis

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We will in this paper present methods and algorithms for estimating two-view geometry based on an orthographic camera model. We use a previously neglected nonlinear criterion on rigidity to estimate the calibrated essential matrix. We give efficient algorithms for estimating it minimally (using only three point correspondences), in a least squares sense (using four or more point correspondences), and optimally with respect to the number of inliers. The inlier-optimal algorithm is based on a three-point solver and gives a fourth-order polynomial time algorithm. These methods can be used as building blocks to robustly find inlier correspondences in the presence of high degrees of outliers. We show experimentally that our methods can be used in many instances, where the orthographic camera model isn't generally used. A case of special interest is situations with repetitive structures, which give high amounts of outliers in the initial feature point matching.

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Section: Semisynthetic Datamentioning

confidence: 99%

Two-View Orthographic Epipolar Geometry: Minimal and Optimal Solvers

Oskarsson

2017

J Math Imaging Vis

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“…Minimal cases for low rank matrix factorization, for missing data, were investigated in [27]. Indoor localization is a currently a key issue, from needing to know the location of objects using Ultra-Wide Band beacons to finding the location of mobile phones with Wi-Fi when a GPS signal cannot be acquired.…”

Section: Introductionmentioning

confidence: 99%

Robust time-of-arrival self calibration with missing data and outliers

Batstone

Oskarsson

Åström

2016

2016 24th European Signal Processing Conference (EUSIPCO)

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The problem of estimating receiver-sender node positions from measured receiver-sender distances is a key issue in different applications such as microphone array calibration, radio antenna array calibration, mapping and positioning using ultrawideband and mapping and positioning using round-trip-time measurements between mobile phones and Wi-Fi-units. Thanks to recent research in this area we have an increased understanding of the geometry of this problem. In this paper, we study the problem of missing information and the presence of outliers in the data. We propose a novel hypothesis and test framework that efficiently finds initial estimates of the unknown parameters and combine such methods with optimization techniques to obtain accurate and robust systems. The proposed systems are evaluated against current state-of-the-art methods on a large set of benchmark tests. This is evaluated further on Wi-Fi roundtrip time and ultra-wideband measurements to give a realistic example of self calibration for indoor localization.

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