Iterative Error Bound Minimisation for AAM Alignment

The active appearance model (AAM) is a powerful generative method used for modelling and segmenting de-formable visual objects. Linear iterative methods have proven to be an efficient alignment method for the AAM when initialisation is close to the optimum. However, current methods are plagued with the requirement to adapt these linear update models to the problem at hand when the class of visual object being modelled exhibits large variations in shape and texture. In this paper, we present a new precomputed parameter update scheme which is designed to reduce the error bound over the model parameters at every iteration. Compared to traditional update methods, our method boasts significant improvements in both convergence frequency and accuracy for complex visual objects whilst maintaining efficiency

[1]  Timothy F. Cootes,et al.  Interpreting face images using active appearance models , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[2]  Mikkel B. Stegmann,et al.  Fast Registration of Cardiac Perfusion MRI , 2003 .

[3]  Lars Kai Hansen,et al.  Mapping from Speech to Images Using Continuous State Space Models , 2004, MLMI.

[4]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Simon Baker,et al.  Equivalence and efficiency of image alignment algorithms , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Aziz Umit Batur,et al.  Adaptive active appearance models , 2005, IEEE Transactions on Image Processing.

[7]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[8]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[9]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[10]  Avinash C. Kak,et al.  Accurate 3D Tracking of Rigid Objects with Occlusion Using Active Appearance Models , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[11]  Mikkel B. Stegmann,et al.  The IMM Face Database, An Annotated Dataset of 240 Face Images , 2004 .

[12]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.