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
Object Tracking Using Adaptive Color Mixture Models
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Incremental AAM using synthesized illumination images
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
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Because many model-based object representation approaches such as active appearance models (AAMs) use a fixed linear appearance model, they often fail to fit to a novel image that is captured in a different imaging condition from that of training images. To alleviate this problem, we propose to use adaptive linear appearance model that is updated by the incremental principal component analysis (PCA). Because the incremental update algorithm uses a new appearance data that is obtained in an on-line manner, a reliable method to measure the quality of the new data is required not to break the integrity of the appearance model. For this purpose, we modified the adaptive observation model (AOM), which has been used to model the varying appearance of the target object using statistical model such as Gaussian mixtures. Experiment results showed that the incremental AAM that uses adaptive linear appearance model greatly improved the robustness to the varying illumination condition when compared to the traditional AAM.