Using vanishing points for camera calibration
International Journal of Computer Vision
Advanced animation and rendering techniques
Advanced animation and rendering techniques
Active intrinsic calibration using vanishing points
Pattern Recognition Letters
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Vanishing Point Detection by Line Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Metric Rectification for Perspective Images of Planes
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Manhattan World: Compass Direction from a Single Image by Bayesian Inference
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Single-View Metrology: Algorithms and Applications
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Manhattan world: orientation and outlier detection by Bayesian inference
Neural Computation
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Orientation in Manhattan: Equiprojective Classes and Sequential Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient detection of vanishing points using inverted coordinates image space
Pattern Recognition Letters
Camera Calibration from Video of a Walking Human
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric image parsing in man-made environments
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Geometric Image Parsing in Man-Made Environments
International Journal of Computer Vision
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation
International Journal of Computer Vision
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
Corisco: Robust edgel-based orientation estimation for generic camera models
Image and Vision Computing
| Hi-index | 0.00 |
We present a completely automatic method for obtaining the approximate calibration of a camera (alignment to a world frame and focal length) from a single image of an unknown scene, provided only that the scene satisfies a Manhattan world assumption. This assumption states that the imaged scene contains three orthogonal, dominant directions, and is often satisfied by outdoor or indoor views of man-made structures and environments.The proposed method combines the calibration likelihood introduced in [5] with a stochastic search algorithm to obtain a MAP estimate of the camera's focal length and alignment. Results on real images of indoor scenes are presented. The calibrations obtained are less accurate than those from standard methods employing a calibration pattern or multiple images. However, the outputs are certainly good enough for common vision tasks such as tracking. Moreover, the results are obtained without any user intervention, from a single image, and without use of a calibration pattern.