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Point Distribution Model: Computer Vision, Statistical Shape Analysis, Image Segmentation
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(Buch) |
Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!
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| Please note that the content of this book primarily consists of articles
available from Wikipedia or other free sources online. The point
distribution model is a model for representing the mean geometry of a
shape and some statistical modes of geometric variation inferred from a
training set of shapes. It has been developed by Cootes, Taylor et al.
and became a standard in computer vision for the statistical study of
shape[3] and for segmentation of medical images where shape priors
really help interpretation of noisy and low-contrasted pixels/voxels.
The latter point leads to active shape models (ASM) and active
appearance models (AAM). Point distribution models rely on landmark
points. A landmark is an annotating point posed by an anatomist onto a
given locus for every shape instance across the training set population.
For instance, the same landmark will designate the tip of the index in a
training set of 2D hands outlines. Principal component analysis (PCA),
for instance, is a relevant tool for studying correlations of movement
between groups of landmarks among the training set population. |
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i.d.R. innert 7-14 Tagen versandfertig
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