Localized Principal Component Analysis based Curve Evolution: A Divide and Conquer Approach

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 in...

• Type: Journal
• Year: 2011
• Where: ICCV
• Authors: Vikram Appia, Balaji Ganapathy, Tracy Faber, Anthony Yezzi
• Keywords: Computer Vision, Global Segmentation, ICCV 2011, Principal Component Analysis, Target Regions