Active Appearance Models: Theory and Cases

In this paper, we present a general approach towards image segmentation using the deformable model Active Appearance Model (AAM) as proposed by Cootes et al. A priori knowledge is learned through observation of shape and texture variation in a training set and is used to obtain a compact object class description, which can be used to rapidly search images for new object instances. An overview of the theory behind AAMs is given followed by an improved initialization scheme, thus making the AAMs fully automated. Finally, two cases are presented. It is demonstrated that AAMs can successfully segment bone structures in radiographs of human hands and structures of the human heart in 2D extracts of 4D cardiovascular magnetic resonance images. The observed mean point location accuracy was 1.0 and 1.3 pixels, respectively.