Binocular Image Flows: Steps Toward Stereo-Motion Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image-flow computation: an estimation-theoretic framework and a unified perspective
CVGIP: Image Understanding
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchy of cameras for 3D photography
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Two-View Multibody Structure-and-Motion with Outliers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Roadmap to the Integration of Early Visual Modules
International Journal of Computer Vision
Nonparametri information fusion for motion estimation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Nonparametric estimation of multiple structures with outliers
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
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Information fusion algorithms have been successful in many vision tasks such as stereo, motion estimation, registration and robot localization. Stereo and motion image analysis are intimately connected and can provide complementary information to obtain robust estimates of scene structure and motion. We present an information fusion based approach for multi-camera and multi-body structure and motion that combines bottom-up and top-down knowledge on scene structure and motion. The only assumption we make is that all scene motion consists of rigid motion. We present experimental results on synthetic and nonsynthetic data sets, demonstrating excellent performance compared to binocular based state-of-the-art approaches for structure and motion.