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Paulo R.S. Mendonça, KwanYee K. Wong, Roberto Cipolla, "Epipolar Geometry from Profiles under Circular Motion," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 604616, June, 2001.  
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@article{ 10.1109/34.927461, author = {Paulo R.S. Mendonça and KwanYee K. Wong and Roberto Cipolla}, title = {Epipolar Geometry from Profiles under Circular Motion}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {23}, number = {6}, issn = {01628828}, year = {2001}, pages = {604616}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.927461}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Epipolar Geometry from Profiles under Circular Motion IS  6 SN  01628828 SP604 EP616 EPD  604616 A1  Paulo R.S. Mendonça, A1  KwanYee K. Wong, A1  Roberto Cipolla, PY  2001 KW  Structure and motion KW  epipolar geometry KW  profiles KW  apparent contours KW  circular motion. VL  23 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
Abstract—This paper addresses the problem of motion estimation from profiles (also known as apparent contours) of an object rotating on a turntable in front of a single camera. Its main contribution is the development of a practical and accurate technique for solving this problem from profiles alone, which is precise enough to allow for the reconstruction of the shape of the object. No correspondences between points or lines are necessary, although the method proposed can be used equally when these features are available without any further adaptation. Symmetry properties of the surface of revolution swept out by the rotating object are exploited to obtain the image of the rotation axis and the homography relating epipolar lines in two views in a robust and elegant way. These, together with geometric constraints for images of rotating objects, are then used to obtain first the image of the horizon, which is the projection of the plane that contains the camera centers, and then the epipoles, thus fully determining the epipolar geometry of the image sequence. The estimation of the epipolar geometry by this sequential approach (image of rotation axis—homography—image of the horizon—epipoles) avoids many of the problems usually found in other algorithms for motion recovery from profiles. In particular, the search for the epipoles, by far the most critical step, is carried out as a simple onedimensional optimization problem. The initialization of the parameters is trivial and completely automatic for all stages of the algorithm. After the estimation of the epipolar geometry, the Euclidean motion is recovered using the fixed intrinsic parameters of the camera obtained either from a calibration grid or from selfcalibration techniques. Finally, the spinning object is reconstructed from its profiles using the motion estimated in the previous stage. Results from real data are presented, demonstrating the efficiency and usefulness of the proposed methods.
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