Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Pose: The Link between Weak Perspective,Paraperspective, and Full Perspective
International Journal of Computer Vision
Solution of the matrix equation AX + XB = C [F4]
Communications of the ACM
The ubiquitous Kronecker product
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. III: linear algebra
Linear Multi View Reconstruction and Camera Recovery Using a Reference Plane
International Journal of Computer Vision
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pose Estimation for Multiple Camera Systems
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A Probabilistic Framework for Joint Head Tracking and Pose Estimation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Multi-View Geometry for General Camera Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multiple View Geometry and the L_"-norm
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
RANSAC for (Quasi-)Degenerate data (QDEGSAC)
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
The Registration Problem Revisited: Optimal Solutions From Points, Lines and Planes
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A generic structure-from-motion framework
Computer Vision and Image Understanding - Special issue on omnidirectional vision and camera networks
Real-Time Multi-View Face Detection and Pose Estimation in Video Stream
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Head pose estimation in seminar room using multi view face detectors
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Practical global optimization for multiview geometry
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Estimating the lecturer's head pose in seminar scenarios – a multi-view approach
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Fast object tracking using adaptive block matching
IEEE Transactions on Multimedia
Arbitrary viewpoint video synthesis from multiple uncalibrated cameras
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Three-dimensional modeling from two-dimensional video
IEEE Transactions on Image Processing
An efficient two-step solution for vision-based pose determination of a parallel manipulator
Robotics and Computer-Integrated Manufacturing
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In this paper, we introduce a method to estimate the object's pose from multiple cameras. We focus on direct estimation of the 3D object pose from 2D image sequences. Scale-Invariant Feature Transform (SIFT) is used to extract corresponding feature points from adjacent images in the video sequence. We first demonstrate that centralized pose estimation from the collection of corresponding feature points in the 2D images from all cameras can be obtained as a solution to a generalized Sylvester's equation. We subsequently derive a distributed solution to pose estimation from multiple cameras and show that it is equivalent to the solution of the centralized pose estimation based on Sylvester's equation. Specifically, we rely on collaboration among the multiple cameras to provide an iterative refinement of the independent solution to pose estimation obtained for each camera based on Sylvester's equation. The proposed approach to pose estimation from multiple cameras relies on all of the information available from all cameras to obtain an estimate at each camera even when the image features are not visible to some of the cameras. The resulting pose estimation technique is therefore robust to occlusion and sensor errors from specific camera views. Moreover, the proposed approach does not require matching feature points among images from different camera views nor does it demand reconstruction of 3D points. Furthermore, the computational complexity of the proposed solution grows linearly with the number of cameras. Finally, computer simulation experiments demonstrate the accuracy and speed of our approach to pose estimation from multiple cameras.