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- Active shape model
- Active contour model
- Statistical shape analysis
- Active appearance model
- 3D Morphable Model
- Centripetal Catmull–Rom spline
- Eigenface
- ASM
- Procrustes analysis
- General-purpose computing on graphics processing units
- Active shape model - Wikipedia
- An Introduction to Active Shape Models - University of …
- Active Shape Model (ASM) and Active Appearance Model (AAM)
- Active Shape Model and Its Application to Face Alignment
- Active Shape Models-Their Training and Application
- An Introduction to Active Shape Models - Clemson University
- Active Shape Models - University of Manchester
- Active Appearance Models - University of California, Berkeley
- Shape Analysis Statistical Shape Models - Pennsylvania …
- Active Shape Models — ‘Smart Snakes’ - Springer
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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, developed by Tim Cootes and Chris Taylor in 1995. 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.
The ASM works by alternating the following steps:
Generate a suggested shape by looking in the image around each point for a better position for the point. This is commonly done using what is called a "profile model", which looks for strong edges or uses the Mahalanobis distance to match a model template for the point.
Conform the suggested shape to the point distribution model, commonly called a "shape model" in this context. The figure to the right shows an example.
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" method, since it is an analog to an active contour model which would respect explicit shape constraints.
See also
Procrustes analysis
Point distribution model
References
External links
Matlab code open-source ASM implementation.
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).
ASMlib-OpenCV, An open source C++/OpenCV implementation of ASM.
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Active Shape Model Segmentation Codes - mathbertyl
Flowchart of active shape model segmentation. | Download Scientific Diagram
Active Model Management
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Calculation flow of the active shape model. | Download Scientific Diagram
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active shape model
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Active shape model - Wikipedia
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]
An Introduction to Active Shape Models - University of …
Here we describe the statistical models of shape and appearance used to repre-sent objects in images. A model is trained from a set of images annotated by a human expert. By analysing the variations in shape and appearance over the training set, a model is built which can mimic this variation. To interpret a new
Active Shape Model (ASM) and Active Appearance Model (AAM)
Jan 26, 2012 · This is an example of the basic Active Shape Model (ASM) and also the Active Appearance Model (AAM) as introduced by Cootes and Taylor, 2D and 3D with multi-resolution approach, color image support and improved edge finding method.
Active Shape Model and Its Application to Face Alignment
Active Shape Model (ASM) is a model-based method, which makes use of a prior model of what is expected in the image, and typically attempts to find the best match position between the model and the data in a new image.
Active Shape Models-Their Training and Application
Jan 1, 1995 · We describe a method for building models by learning patterns of variability from a training set of correctly annotated images. These models can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes).
An Introduction to Active Shape Models - Clemson University
Here we describe the statistical models of shape and appearance used to repre-sent objects in images. A model is trained from a set of images annotated by a human expert. By analysing the variations in shape and appearance over the training set, a model is built which can mimic this variation. To interpret a new
Active Shape Models - University of Manchester
Active Shape Models are statistical models of the shapes 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.
Active Appearance Models - University of California, Berkeley
Active Shape Model is matched to boundary features in the image, then a separate eigenface model is used to reconstruct the texture (in the graphics sense) in a shape-normalized frame.
Shape Analysis Statistical Shape Models - Pennsylvania …
Active Shape Models • Suppose we have a statistical shape model – Trained from sets of examples • How do we use it to interpret new images? • Use an “Active Shape Model” • Iterative method of matching model to image (similar to active contours)
Active Shape Models — ‘Smart Snakes’ - Springer
We describe ‘Active Shape Models’ which iteratively adapt to refine estimates of the pose, scale and shape of models of image objects. The method uses flexible models derived from sets of training examples.