Active shape models (ASMs) are statistical models of the shape of objects which iteratively deform to fit to an example of the object in a new image. The shapes are constrained by the PDM (Point Distribution Model) Statistical Shape Model to vary only in ways seen in a training set of labelled examples. The shape of an object is represented by a set of points (controlled by the shape model). The ASM algorithm aims to match the model to a new image. It works by alternating the following steps:

• Look in the image around each point for a better position for that point.
• Update the model parameters to best match to these new found positions.

To locate a better position for each point one can look for strong edges, or a match to a statistical model of what is expected at the point. The technique has been widely used to analyse images of faces, mechanical assemblies and medical images (in 2D and 3D).

An Active Appearance Model (AAM) is a computer vision algorithm for matching a statistical model of object shape and appearance to a new image. They are built during a training phase. A set of images together with coordinates of landmarks, that appear in all of the images is provided by the training supervisor. The approach is widely used for matching and tracking faces and for medical image interpretation. The algorithm uses the difference between the current estimate of appearance and the target image to drive an optimization process. By taking advantage of the least squares techniques, it can match to new images very swiftly.