Entropy-Optimized Texture Models
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Gabor texture in active appearance models
Neurocomputing
Toward fully automated face pose estimation
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
Texture representation in AAM using Gabor wavelet and local binary patterns
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A review of active appearance models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adapted active appearance models
Journal on Image and Video Processing
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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The Active Appearance Model (AAM) algorithm matches statistical models of shape and texture to images rapidly by assuming a linear relationship between the texture residual and changes in the model parameters. When the texture is represented as raw intensity values, this has been shown to be a reasonable approximation in many cases. However, models built on them are sensitive to changes in illumination conditions. This paper examines the effect of using different representations of image texture to improve the accuracy and robustness of the AAM search. We show that normalising the gradient images by non-linear techniques can give much improved matching with higher accuracy and a wider effective range of convergence.