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Online Learning of Correspondences between Images
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-6096-3648
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
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2013 (English)In: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 35, no 1, 118-129 p.Article in journal (Refereed) Published
Abstract [en]

We propose a novel method for iterative learning of point correspondences between image sequences. Points moving on surfaces in 3D space are projected into two images. Given a point in either view, the considered problem is to determine the corresponding location in the other view. The geometry and distortions of the projections are unknown as is the shape of the surface. Given several pairs of point-sets but no access to the 3D scene, correspondence mappings can be found by excessive global optimization or by the fundamental matrix if a perspective projective model is assumed. However, an iterative solution on sequences of point-set pairs with general imaging geometry is preferable. We derive such a method that optimizes the mapping based on Neyman's chi-square divergence between the densities representing the uncertainties of the estimated and the actual locations. The densities are represented as channel vectors computed with a basis function approach. The mapping between these vectors is updated with each new pair of images such that fast convergence and high accuracy are achieved. The resulting algorithm runs in real-time and is superior to state-of-the-art methods in terms of convergence and accuracy in a number of experiments.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013. Vol. 35, no 1, 118-129 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-79260DOI: 10.1109/TPAMI.2012.65ISI: 000311127700012OAI: oai:DiVA.org:liu-79260DiVA: diva2:539757
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DIPLECSGARNICSELLIITETTCUASFOCUSCADICS
Note

funding agencies|EC|215078247947|ELLIIT||Strategic Area for ICT research||CADICS||Swedish Government||Swedish Research Council||CUAS||FOCUS||Swedish Foundation for Strategic Research||

Available from: 2012-07-05 Created: 2012-07-05 Last updated: 2017-12-07

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Felsberg, MichaelLarsson, FredrikWiklund, JohanAhlberg, Jörgen

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