Active shape models—their training and application
Computer Vision and Image Understanding
Matrix computations (3rd ed.)
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Updating the singular value decomposition
Journal of Computational and Applied Mathematics
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Efficient illumination independent appearance-based face tracking
Image and Vision Computing
Adaptive Object Tracking Based on an Effective Appearance Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential Karhunen-Loeve basis extraction and its application to images
IEEE Transactions on Image Processing
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Maximising audiovisual correlation with automatic lip tracking and vowel based segmentation
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
| Hi-index | 0.00 |
Many object tracking methods based on Adaptive Appearance Models (online learning methods) have been developed in recent years. One problem that can be found with these methods is how to learn variations in object appearance without errors in the image sequence. This paper introduces a novel method, in which a solution to remove learning errors by using an offline learning is proposed; in addition, our method can be thought of as a generalization of Active Appearance Models, in which the shape model is built manually and object appearance are modeled sequentially in video sequences. Experimental results on lip tracking show that our proposed tracker is functioning accurately.