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<bibtex:entry id="brunet2010vmvroi">
  <bibtex:inproceedings>
    <bibtex:author>Florent Brunet and Adrien Bartoli and Nassir Navab and R&#38;&#35;x00E9;my Malgouyres</bibtex:author>
    <bibtex:editor>Reinhard Koch and Andreas Kolb and Christof Rezk&#45;Salama</bibtex:editor>
    <bibtex:title>Direct Image Registration without Region of Interest</bibtex:title>
    <bibtex:booktitle>International Workshop on Vision&#44; Modeling&#44; and Visualization (VMV)</bibtex:booktitle>

    <bibtex:publisher>Eurographics Association</bibtex:publisher>
    <bibtex:year>2010</bibtex:year>

    <bibtex:month>November</bibtex:month>

    <bibtex:pages>323-330</bibtex:pages>






    <bibtex:address>Siegen (Germany)</bibtex:address>

    <bibtex:abstract>Standard 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 &#39;chicken&#45;and&#45;egg&#39; 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.&#10;&#10;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&#45;target pixels as outliers. We define the off&#45;target pixels as those pixels of the source image mapped outside the target image by the current warp. We use the classical robust M&#45;estimation framework to handle both the off&#45;target pixels and the usual outliers caused&#44; for instance&#44; by occlusions. With our formulation&#44; the true image overlap is defined as the set of inliers.&#10;&#10;Experiments on synthetic and real data with the homography and Free&#45;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.</bibtex:abstract>
    <bibtex:url>http://www.brnt.eu/publications/brunet2010vmvroi.pdf</bibtex:url>
    <bibtex:doi>http://dx.doi.org/10.2312/PE/VMV/VMV10/323&#45;330</bibtex:doi>











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