Binocular Image Flows: Steps Toward Stereo-Motion Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for subpixel registration
Computer Vision, Graphics, and Image Processing
Using Real-Time Stereo Vision for Mobile Robot Navigation
Autonomous Robots
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Real Time Fusion of Motion and Stereo Using Flow/Depth Constraint for Fast Obstacle Detection
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Depth Discontinuities by Pixel-to-Pixel Stereo
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Sampling the Disparity Space Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Real-time stereo vision using semi-global matching on programmable graphics hardware
ACM SIGGRAPH 2006 Sketches
Integrating disparity images by incorporating disparity rate
RobVis'08 Proceedings of the 2nd international conference on Robot vision
6D-vision: fusion of stereo and motion for robust environment perception
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Dense Stereo-Based ROI Generation for Pedestrian Detection
Proceedings of the 31st DAGM Symposium on Pattern Recognition
High-Level Fusion of Depth and Intensity for Pedestrian Classification
Proceedings of the 31st DAGM Symposium on Pattern Recognition
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Stereo vision is a key technology for understanding natural scenes. Most research concentrates on single image pairs. However, in robotic and intelligent vehicles applications image sequences have to be analyzed. The paper shows that an appropriate evaluation in time gives much better results than classical frame-by-frame reconstructions. We start with the state-of-the art in real-time stereo analysis and describes novel techniques to increase the sub-pixel accuracy. Secondly, we show that static scenes seen from a moving observer can be reconstructed with significantly higher precision, if the stereo correspondences are integrated over time. Finally, an optimal fusion of stereo and optical flow, called 6D-Vision, is described that directly estimates position and motion of tracked features, even if the observer is moving. This eases the detection and tracking of moving obstacles significantly.