• Comparative Analysis of Kernel Methods for Statistical Shape Learning 

      Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen R. (Georgia Institute of TechnologySpringer Verlag, 2006-05)
      Prior knowledge about shape may be quite important for image segmentation. In particular, a number of different methods have been proposed to compute the statistics on a set of training shapes, which are then used for a ...
    • Graph cut segmentation with nonlinear shape priors 

      Malcolm, James G.; Rathi, Yogesh; Tannenbaum, Allen R. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2007-09)
      Graph cut image segmentation with intensity information alone is prone to fail for objects with weak edges, in clutter, or under occlusion. Existing methods to incorporate shape are often too restrictive for highly varied ...
    • Shape-Based Approach to Robust Image Segmentation using Kernel PCA 

      Dambreville, Samuel; Rathi, Yogesh; Tannenbaum, Allen R. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2006-06)
      Segmentation involves separating an object from the background. In this work, we propose a novel segmentation method combining image information with prior shape knowledge, within the level-set framework. Following the ...
    • Statistical Shape Analysis using Kernel PCA 

      Rathi, Yogesh; Dambreville, Samuel; Tannenbaum, Allen R. (Georgia Institute of TechnologySociety of Photo-Optical Instrumentation Engineers, 2006-01)
      Mercer kernels are used for a wide range of image and signal processing tasks like de-noising, clustering, discriminant analysis etc. These algorithms construct their solutions in terms of the expansions in a high-dimensional ...