Automatic Construction of Active Appearance Models
Image coding is the task of representing a set of images as accurately as possible using a fixed number of parameters. One well known example is the linear coding problem that leads to Principal Components Analysis (PCA). Although optimal in a certain sense, PCA has limited coding power. A large number of parameters are often required to code a set of images accurately. In this project we have developed an algorithm for image coding using Active Appearance Models (AAMs). AAMs are a class of generative non-linear models (although linear in both shape and appearance) which have received a great deal of recent attention in the computer vision literature. Our algorithm can also be interpreted as an automatic algorithm for the (unsupervised) learning of Active Appearance Models.
