Direct Linear Sub-Pixel Correlation by Incorporation of Neighbor Pixels' Information and Robust Estimation of Window Transformation

Zhong-Dan Lan 1 Roger Mohr 1
1 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : Standard methods for sub-pixel matching are iterative and nonlinear; they are also sensitive to false initialization and window deformation. In this paper, we present a linear method that incorporates information from neighboring pixels. Two algorithms are presented: one ‘fast' and one ‘robust'. They both start from an initial rough estimate of the matching. The fast one is suitable for pairs of images requiring negligible window deformation. The robust method is slower but more general and more precise. It eliminates false matches in the initialization by using robust estimation of the local affine deformation. The first algorithm attains an accuracy of 0.05 pixels for interest points and 0.06 for random points in the translational case. For the general case, if the deformation is small, the second method gives an accuracy of 0.05 pixels; while for large deformation, it gives an accuracy of about 0.06 pixels for points of interest and 0.10 pixels for random points. They are very few false matches in all cases, even if there are many in the initialization.
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Machine Vision and Applications, Springer Verlag, 1998, 10 (5/6), pp.256--268. 〈http://www.springerlink.com/content/1nptx13b54e404bu/〉. 〈10.1007/s001380050077〉
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Dernière modification le : mercredi 11 avril 2018 - 01:56:20

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Zhong-Dan Lan, Roger Mohr. Direct Linear Sub-Pixel Correlation by Incorporation of Neighbor Pixels' Information and Robust Estimation of Window Transformation. Machine Vision and Applications, Springer Verlag, 1998, 10 (5/6), pp.256--268. 〈http://www.springerlink.com/content/1nptx13b54e404bu/〉. 〈10.1007/s001380050077〉. 〈inria-00548340〉

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