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). It is closely related to the Active Appearance Model. It is also known as a 'Smart Snake'[1] method, since it is analog to an Active contour which would respect explicit shape constraints.
Some reference articles
[1]: @article{ cooper95asp_training, author = "D.H. Cooper and T.F. Cootes and C.J. Taylor and J. Graham", title = "Active shape models - their training and application", journal = "Computer Vision and Image Understanding", number = 61, pages = "38--59", year = 1995 }
See also
External links
- Description of AAMs from Manchester University.
- Tim Cootes' home page (one of the original co-inventors of ASMs).
- Source code for ASMs (the "stasm" library).
