Content deleted Content added
m links |
No edit summary |
||
Line 1:
'''Active shape models (ASMs)''' are [[statistical model]]s 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 <ref name=Cootes>{{citejournal| author=T.F. Cootes and C.J. Taylor and D.H. Cooper and J. Graham| title=Active shape models - their training and application| journal=Computer Vision and Image Understanding| number=61| pages=38--59| year=1995}} [http://www.cs.ualberta.ca/~nray1/CMPUT615/Snake/cootes_cviu95.pdf]</ref>. 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
Line 5:
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|Mahalanobis distance]] to detect a better position for each landmark point <ref name=Cootes/>.
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”<ref
==References==
| |||