Error Analysis in Stereo Determination of 3-D Point Positions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Analysis of Stereo Quantization Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust regression methods for computer vision: a review
International Journal of Computer Vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Computer Vision and Image Understanding
On Limit Properties in Digitization Schemes
Journal of the ACM (JACM)
Robust Parameter Estimation in Computer Vision
SIAM Review
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Discrete linear objects in dimension n: the standard model
Graphical Models - Special issue: Discrete topology and geometry for image and object representation
Predicting corresponding region in a third view using discrete epipolar lines
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
| Hi-index | 0.00 |
The epipolar geometry, which lies in the basis of 3D reconstruction techniques in the field of computer vision, is formulated in continuous spaces and gives geometric relationships between different views of a point in space. In applications, however, we cannot deal with points themselves in digital images. This is because digital images involve some digitization process and the smallest unit in digital images is a pixel. In this paper, we propose a discrete version of the epipolar geometry, called the discrete epipolar geometry, that gives geometric relationships between pixels rather than points. We then apply this discrete epipolar geometry to 3D reconstruction.