International Journal of Computer Vision

, Volume 38, Issue 1, pp 75–91 | Cite as

Morphable Surface Models

  • Christian R. Shelton

Abstract

We describe a novel automatic technique for finding a dense correspondence between a pair of n-dimensional surfaces with arbitrary topologies. This method employs a different formulation than previous correspondence algorithms (such as optical flow) and includes images as a special case. We use this correspondence algorithm to build Morphable Surface Models (an extension of Morphable Models) from examples. We present a method for matching the model to new surfaces and demonstrate their use for analysis, synthesis, and clustering.

computer vision learning correspondence morphable models surface matching 

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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Christian R. Shelton
    • 1
  1. 1.Center for Biological and Computational Learning, Artificial Intelligence LaboratoryM.I.T.CambridgeUSA

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