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dc.contributor.authorBrunet, Florenten_US
dc.contributor.authorBartoli, Adrienen_US
dc.contributor.authorNavab, Nassiren_US
dc.contributor.authorMalgouyres, Rémyen_US
dc.contributor.editorReinhard Koch and Andreas Kolb and Christof Rezk-Salamaen_US
dc.date.accessioned2014-02-01T16:18:51Z
dc.date.available2014-02-01T16:18:51Z
dc.date.issued2010en_US
dc.identifier.isbn978-3-905673-79-1en_US
dc.identifier.urihttp://dx.doi.org/10.2312/PE/VMV/VMV10/323-330en_US
dc.description.abstractStandard direct image registration consists in estimating the geometric warp between a source and a target images by maximizing the photometric similarity for the pixels of a Region of Interest (ROI). The ROI must be included in the real overlap between the images otherwise standard registration algorithms fail. Determining a proper ROI is a hard 'chicken-and-egg' problem since the overlap is only known after a successful registration. Almost all algorithms in the literature consider that the ROI is given. This is generally either inconvenient or unreliable. In this paper we propose a new method that registers two images without using a ROI. The key idea of our method is to consider the off-target pixels as outliers. We define the off-target pixels as those pixels of the source image mapped outside the target image by the current warp. We use the classical robust M-estimation framework to handle both the off-target pixels and the usual outliers caused, for instance, by occlusions. With our formulation, the true image overlap is defined as the set of inliers. Experiments on synthetic and real data with the homography and Free-Form Deformation show that our method outperforms standard approaches in terms of accuracy and robustness while precisely retrieving the overlap in the source and target images.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.4.3 [Computer Graphics]: Image Processing and Computer Vision-Registrationen_US
dc.titleDirect Image Registration without Region of Interesten_US
dc.description.seriesinformationVision, Modeling, and Visualization (2010)en_US


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  • VMV10
    ISBN 978-3-905673-79-1

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