Parallel guessing: a strategy for high-speed computation
Pattern Recognition
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
CVGIP: Image Understanding
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
Detection of piecewise-linear signals by the randomized Hough transform
Pattern Recognition Letters
On accurate and robust estimation of fundamental matrix
Computer Vision and Image Understanding
Motion analysis by random sampling and voting process
Computer Vision and Image Understanding
MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Detection of spatial points and lines by random sampling and voting procedure
Pattern Recognition Letters
Performance Analysis of Shape Recovery by Random Sampling and Voting
Proceedings of the Theoretical Foundations of Computer Vision, TFCV on Performance Characterization in Computer Vision
On the geometry and algebra of the point and line correspondences between N images
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
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In the series of papers, we proposed a method for three-dimensional reconstruction from an image sequence without predetecting feature correspondences. In the method, we first collect all images and sample data, and second apply the reconstruction procedure. Therefore, the method is categorized into an off-line algorithm. In this paper, we deal with an on-line algorithm for three-dimensional reconstruction, if we sequentially measure images. Our method is based on the property that points and lines in space are uniquely computed from their projections between two images and among three images, respectively, if a camera system is calibrated. Using these property, our method determines both feature correspondences and three-dimensional positions of points and lines on an object.