Description: Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach.
Basic idea:
The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions.
After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour.
Literature:
- Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I
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File list (Check if you may need any files):
ASM_example.m
ApplyModel.m
Fotos
.....\test001.jpg
.....\train001.jpg
.....\train001.mat
.....\train002.jpg
.....\train002.mat
.....\train003.jpg
.....\train003.mat
.....\train004.jpg
.....\train004.mat
.....\train005.jpg
.....\train005.mat
.....\train006.jpg
.....\train006.mat
.....\train007.jpg
.....\train007.mat
.....\train008.jpg
.....\train008.mat
.....\train009.jpg
.....\train009.mat
.....\train010.jpg
.....\train010.mat
Functions
.........\align_data.m
.........\align_data_inverse.m
.........\DrawContourGui.fig
.........\DrawContourGui.m
.........\GetContourNormals.m
.........\getProfileAndDerivatives.m
.........\linspace_multi.m
.........\LoadDataSetNiceContour.m
.........\PCA.m
MakeAppearanceModel.m
MakeShapeModel.m
license.txt
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