Title:
Localized Principal Component Analysis based Curve Evolution: A Divide and Conquer Approach
Localized Principal Component Analysis based Curve Evolution: A Divide and Conquer Approach
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Author(s)
Appia, Vikram
Ganapathy, Balaji
Yezzi, Anthony
Faber, Tracy
Ganapathy, Balaji
Yezzi, Anthony
Faber, Tracy
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Abstract
We propose a novel localized principal component analysis (PCA) based curve evolution approach which evolves
the segmenting curve semi-locally within various target regions (divisions) in an image and then combines these locally accurate segmentation curves to obtain a global segmentation. The training data for our approach consists
of training shapes and associated auxiliary (target) masks. The masks indicate the various regions of the shape exhibiting highly correlated variations locally which may be rather independent of the variations in the distant parts of the global shape. Thus, in a sense, we are clustering the variations exhibited in the training data set. We then use
a parametric model to implicitly represent each localized segmentation curve as a combination of the local shape priors obtained by representing the training shapes and the
masks as a collection of signed distance functions. We also propose a parametric model to combine the locally evolved
segmentation curves into a single hybrid (global) segmentation. Finally, we combine the evolution of these semi-local and global parameters to minimize an objective energy function. The resulting algorithm thus provides a globally accurate solution, which retains the local variations in shape. We present some results to illustrate how our approach performs better than the traditional approach with
fully global PCA.
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Date Issued
2011-11
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