Iterative Error Bound Minimisation for AAM Alignment
Authors: Jason Saragih and Roland Göcke
Presented by Jason Saragih at the 18th International Conference on
Pattern Recognition ICPR2006, Hong Kong, 20-24 August 2006
Abstract
The Active Appearance Model (AAM) is a powerful generative method
used for modelling and segmenting deformable 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.