Active shape models—their training and application
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Evaluating Error Functions for Robust Active Appearance Models
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Hi-index | 0.00 |
With the aid of AAMs search algorithm, Active Appearance Models (AAMs) can represent non-rigid image objects with shape and texture variations well. However, the performance of the traditional AAMs search algorithm(TAAMS) is limited by its assumption that the error function is convex. Therefore, this paper proposes a robust AAMs search algorithm (RAAMS) which combines the multi-pose search (MS) for better pose matching and an estimation mechanism of parameter search direction (EPSD) for more accurate search direction. Moreover, a precaution mechanism of local minimum (PLM) is proposed to avoid the search trapped into the local minimum of the error function. Experimental results show that the proposed algorithm can significantly reduce 36.41% of shape error and 30.81% of texture error between the synthesized instance and target image.