Line-Drawing Interpretation: Bilateral Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Curve matching and stereo calibration
Image and Vision Computing
Surface shape from the deformation of apparent contours
International Journal of Computer Vision
Recognizing general curved objects efficiently
Geometric invariance in computer vision
Projective Reconstruction and Invariants from Multiple Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual motion of curves and surfaces
Visual motion of curves and surfaces
Using Singular Displacements for Uncalibrated Monocular Visual Systems
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Generalised Epipolar Constraints
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Camera Pose Estimation and Reconstruction from Image Profiles under Circular Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Automatic 3D Model Construction for Turn-Table Sequences
SMILE'98 Proceedings of the European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Class-based grouping in perspective images
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Motion from the frontier of curved surfaces
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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This paper addresses the problem of motion recovery from image profiles, in the important case of turntable sequences. No correspondences between points or lines are used. Symmetry properties of surfaces of revolution are exploited to obtain, in a robust and simple way, the image of the rotation axis of the sequence and the homography relating epipolar lines. These, together with geometric constraints for images of rotating objects, are used to obtain epipoles and, consequently, the full epipolar geometry of the camera system. This sequential approach (image of rotation axis -- homography -- 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 for the estimation of the epipolar geometry, is carried out as a one-dimensional optimization problem, with a smooth unimodal cost function. The initialization of the parameters is trivial in all three stages of the algorithm. After the estimation of the epipolar geometry, the motion is recovered using the fixed intrinsic parameters of the camera, obtained either from a calibration grid or from self-calibration techniques. Results from real data are presented, demonstrating the efficiency and practicality of the algorithm.