Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Solution of the simultaneous pose and correspondence problem using Gaussian error model
Computer Vision and Image Understanding
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Localization and Map-Building Using Active Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Object Pose in 25 Lines of Code
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Marker-less Tracking for AR: A Learning-Based Approach
ISMAR '02 Proceedings of the 1st International Symposium on Mixed and Augmented Reality
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on 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
Object Detection Using 2D Spatial Ordering Constraints
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Randomized Trees for Real-Time Keypoint Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Integrating multiple model views for object recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Point matching as a classification problem for fast and robust object pose estimation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Segmenting, modeling, and matching video clips containing multiple moving objects
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Automatic contour model creation out of polygonal CAD models for markerless Augmented Reality
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Feature management for efficient camera tracking
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
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Real-time estimation of a camera’s pose relative to an object is still an open problem. The difficulty stems from the need for fast and robust detection of known objects in the scene given their 3D models, or a set of 2D images or both. This paper proposes a method that conducts a statistical analysis of the appearance of model patches from all possible viewpoints in the scene and incorporates the 3D geometry during both matching and the pose estimation processes. Thereby the appearance information from the 3D model and real images are combined with synthesized images in order to learn the variations in the multiple view feature descriptors using PCA. Furthermore, by analyzing the computed visibility distribution of each patch from different viewpoints, a reliability measure for each patch is estimated. This reliability measure is used to further constrain the classification problem. This results in a more scalable representation reducing the effect of the complexity of the 3D model on the run-time matching performance. Moreover, as required in many real-time applications this approach can yield a reliability measure for the estimated pose. Experimental results show how the pose of complex objects can be estimated efficiently from a single test image.