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'''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 a Statistical Shape Model to vary only in ways seen in a training set of labelled examples.
The '''Active Shape Model''' is a well known contour model used in image processing. An active shape model is constructed by taking a set of points around the outline of an object. As the object moves so do the corresponding points. The distribution of these points can then be examined by [[statistics|statistical]] techniques such as [[principal components analysis]] to find the dominant modes of variation. A combination of these modes is then used to construct a model of how the [[shape]] of an object changes with time.
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
Active shape models are often used for [[biological data]] such as a walking person, and used as a technique for indentifying an object in a scene.
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]].
 
The Active Shape Model Toolkit is a set of software tools for image interpretation developed by Dr [[Tim Cootes]], a research fellow at the Wolfson Image Analysis Unit of the [[University of Manchester]]. The toolkit provides a variety of two-dimensional image analysis techniques intended for applications in industry, medicine and science. The toolkit is available as an add-on for [[MatLab]].
 
==See also==