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Generalized image matching: Statistical learning of physically-based deformations

  • Chahab Nastar
  • Baback Moghaddam
  • Alex Pentland
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1064)

Abstract

We describe a novel approach for image matching based on deformable intensity surfaces. In this approach, the intensity surface of the image is modeled as a deformable 3D mesh in the (x,y,I(x,y)) space. Each surface point has 3 degrees of freedom, thus capturing fine surface changes. A set of representative deformations within a class of objects (e.g. faces) are statistically learned through a Principal Components Analysis, thus providing a priori knowledge about object-specific deformations. We demonstrate the power of the approach by examples such as image matching and interpolation of missing data. Moreover this approach dramatically reduces the computational cost of solving the governing equation for the physically based system by approximately three orders of magnitude.

Keywords

Optical Flow Image Match Intensity Surface Principal Subspace Deformable Mesh 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Chahab Nastar
    • 1
  • Baback Moghaddam
    • 2
  • Alex Pentland
    • 2
  1. 1.INRIA RocquencourtLe Chesnay CédexFrance
  2. 2.MIT Media LaboratoryCambridgeUSA

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