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ICIP
2009
IEEE
14 years 9 months ago
A Markov Random Field Model for Extracting Near-Circular Shapes
We propose a binary Markov Random Field (MRF) model that assigns high probability to regions in the image domain consisting of an unknown number of circles of a given radius. We...
Tamas Blaskovics, Zoltan Kato, and Ian Jermyn
SMI
2008
IEEE
203views Image Analysis» more  SMI 2008»
14 years 4 months ago
SHREC'08 entry: Shape based face recognition with a Morphable Model
We present a method for face recognition by fitting a 3D Morphable Model to shape data. Fitting is done with a a robust nonrigid ICP algorithm. For recognition, it is possible to...
Brian Amberg, Reinhard Knothe, Thomas Vetter
CVPR
2003
IEEE
14 years 12 months ago
Directional Histogram Model for Three-Dimensional Shape Similarity
In this paper, we propose a novel shape representation we call Directional Histogram Model (DHM). It captures the shape variation of an object and is invariant to scaling and rigi...
Xinguo Liu, Robin Sun, Sing Bing Kang, Heung-Yeung...
PAMI
2002
177views more  PAMI 2002»
13 years 9 months ago
Adjusting Shape Parameters Using Model-Based Optical Flow Residuals
We present a method for estimating the shape of a deformable model using the least-squares residuals from a model-based optical flow computation. This method is built on top of an...
Douglas DeCarlo, Dimitris N. Metaxas
CVPR
2008
IEEE
14 years 12 months ago
Computing minimal deformations: application to construction of statistical shape models
Nonlinear registration is mostly performed after initialization by a global, linear transformation (in this work, we focus on similarity transformations), computed by a linear reg...
Darko Zikic, Michael Sass Hansen, Ben Glocker, Ali...