Real and complex analysis, 3rd ed.
Real and complex analysis, 3rd ed.
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Early Visual Learning
Dynamic Vision: From Images to Face Recognition
Dynamic Vision: From Images to Face Recognition
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Learning novel views to a single face image
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Eigenfiltering for Flexible Eigentracking (EFE)
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Multidimensional Morphable Models
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robust parameterized component analysis: theory and applications to 2D facial appearance models
Computer Vision and Image Understanding - Special issue on Face recognition
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models Revisited
International Journal of Computer Vision
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Data Driven Image Models through Continuous Joint Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Nonlinear Errors-in-Variables Models for Computer Vision Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time combined 2D+3D active appearance models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A machine learning approach for deformable guide-wire tracking in fluoroscopic sequences
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Continuous regression for non-rigid image alignment
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
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Image alignment has been a long standing problem in computer vision. Parameterized Appearance Models (PAMs) such as the Lucas-Kanade method, Eigentracking, and Active Appearance Models are commonly used to align images with respect to a template or to a previously learned model. While PAMs have numerous advantages relative to alternate approaches, they have at least two drawbacks. First, they are especially prone to local minima in the registration process. Second, often few, if any, of the local minima of the cost function correspond to acceptable solutions. To overcome these problems, this paper proposes a method to learn a metric for PAMs that explicitly optimizes that local minima occur at and only at the places corresponding to the correct fitting parameters. To the best of our knowledge, this is the first paper to address the problem of learning a metric to explicitly model local properties of the PAMs' error surface. Synthetic and real examples show improvement in alignment performance in comparison with traditional approaches. In addition, we show how the proposed criteria for a good metric can be used to select good features to track.