p-adic Methods in Stereo Vision

  • Patrick Erik Bradley
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


The so-called essential matrix relates corresponding points of two images from the same scene in 3D, and allows to solve the relative pose problem for the two cameras up to a global scaling factor, if the camera calibrations are known. We will discuss how Hensel’s lemma from number theory can be used to find geometric approximations to solutions of the equations describing the essential matrix. Together with recent p-adic classification methods, this leads to RanSaC p , a p-adic version of the classical RANSAC in stereo vision. This approach is motivated by the observation that using p-adic numbers often leads to more efficient algorithms than their real or complex counterparts.


Stereo Vision Point Correspondence Ultrametric Space Epipolar Geometry Essential Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institut für Photogrammetrie und Fernerkundung, Karlsruhe Institut für Technologie (KIT)KarlsruheGermany

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