Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
A fully projective formulation to improve the accuracy of Lowe's pose-estimation algorithm
Computer Vision and Image Understanding
Optical Flow Constraints on Deformable Models with Applications to Face Tracking
International Journal of Computer Vision
Tracking and modeling people in video sequences
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
A Mathematical Introduction to Robotic Manipulation
A Mathematical Introduction to Robotic Manipulation
Dynamic 3-D Scene Analysis Through Synthesis Feedback Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
SoftPOSIT: Simultaneous Pose and Correspondence Determination
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Analysis of Orientation Problems Using Plucker Lines
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Twist Based Acquisition and Tracking of Animal and Human Kinematics
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Stable Real-Time 3D Tracking Using Online and Offline Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Edge and Texture Information for Real-Time Accurate 3D Camera Tracking
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
Pose Estimation of 3D Free-Form Contours
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-dimensional shape knowledge for joint image segmentation and pose estimation
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
IEEE Transactions on Image Processing
Real-time Hybrid Tracking using Edge and Texture Information
International Journal of Robotics Research
Model-Based Motion Capture for Crash Test Video Analysis
Proceedings of the 30th DAGM symposium on Pattern Recognition
Superpipelined high-performance optical-flow computation architecture
Computer Vision and Image Understanding
Detection and Segmentation of Independently Moving Objects from Dense Scene Flow
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Clustered stochastic optimization for object recognition and pose estimation
Proceedings of the 29th DAGM conference on Pattern recognition
Marker-less 3D feature tracking for mesh-based human motion capture
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Robust pose estimation with 3d textured models
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Parallel architecture for hierarchical optical flow estimation based on FPGA
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
On-set performance capture of multiple actors with a stereo camera
ACM Transactions on Graphics (TOG)
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Tracking the 3-D pose of an object needs correspondences between 2-D features in the image and their 3-D counterparts in the object model. A large variety of such features has been suggested in the literature. All of them have drawbacks in one situation or the other since their extraction in the image and/or the matching is prone to errors. In this paper, we propose to use two complementary types of features for pose tracking, such that one type makes up for the shortcomings of the other. Aside from the object contour, which is matched to a free-form object surface, we suggest to employ the optic flow in order to compute additional point correspondences. Optic flow estimation is a mature research field with sophisticated algorithms available. Using here a high quality method ensures a reliable matching. In our experiments we demonstrate the performance of our method and in particular the improvements due to the optic flow.