Active shape model
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, developed by Tim Cootes and Chris Taylor in 1995 [1]. 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 original methodology suggests using the Mahalanobis distance to detect a better position for each landmark point [1].
The technique has been widely used to analyse images of faces, mechanical assemblies and medical images (in 2D and 3D).
It is closely related to the active appearance model. It is also known as a “Smart Snakes”[1] method, since it is analog to an active contour model which would respect explicit shape constraints.
References
See also
External links
- Description of AAMs from Manchester University.
- Tim Coote's home page (one of the original co-inventors of ASMs).
- Source code for ASMs (the "stasm" library).