Efficient MRF Deformation Model for Image Matching


Shekhovtsov, Alexander, Kovtun, Ivan, Hlaváč, Václav
CTU-CMP-2006-08
October, 2006

Abstract

We propose a novel MRF-based model for image matching. Given two images, the task is to estimate a mapping from one image to another, in order to maximize the matching quality. We consider mappings defined by discrete deformation field constrained to preserve 2-dimensional continuity. We approach the corresponding optimization problem by the TRW-S (sequential Tree-reweighted message passing) algorithm [Wainwright-03, Kolmogorov-05]. Our model design allows for a considerably wider class of natural transformation and yields a compact representation of the optimization task. For this model TRW-S algorithm demonstrated nice practical performance on our experiments. We also propose a concise derivation of the TRW-S algorithm as a sequential maximization of the lower bound on the energy function.

Keywords

optical flow, registration, Energy minimization, MRF, message passing, early vision, graphical models

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Bibtex entry

@TechReport{Shekhovtsov-TR-2006-08,
  author = {Shekhovtsov, Alexander and Kovtun, Ivan and 
            Hlav{\'a}{\vc}, V{\'a}clav},
  title = {Efficient {MRF} Deformation Model for Image Matching},
  institution = {Center for Machine Perception, K13133 FEE Czech Technical
                 University},
  address = {Prague, Czech Republic},
  year = {2006},
  month = {October},
  type = {Research Report},
  number = {CTU--CMP--2006--08},
  issn = {1213-2365},
  pages = {12},
  figures = {4},
  psurl = {[Shekhovtsov-TR-2006-08.pdf]},
  annote = {We propose a novel MRF-based model for image
    matching. Given two images, the task is to estimate a mapping from
    one image to another, in order to maximize the matching
    quality. We consider mappings defined by discrete deformation
    field constrained to preserve 2-dimensional continuity. We
    approach the corresponding optimization problem by the TRW-S
    (sequential Tree-reweighted message passing) algorithm
    [Wainwright-03, Kolmogorov-05]. Our model design allows for a
    considerably wider class of natural transformation and yields a
    compact representation of the optimization task. For this model
    TRW-S algorithm demonstrated nice practical performance on our
    experiments. We also propose a concise derivation of the TRW-S
    algorithm as a sequential maximization of the lower bound on the
    energy function.},
  keywords = {optical flow, registration, Energy minimization, MRF,
    message passing, early vision, graphical models},
}