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Deformed Lattice Discovery Via Efficient Mean-Shift Belief Propagation

  • Minwoo Park
  • Robert T. Collins
  • Yanxi Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)

Abstract

We introduce a novel framework for automatic detection of repeated patterns in real images. The novelty of our work is to formulate the extraction of an underlying deformed lattice as a spatial, multi-target tracking problem using a new and efficient Mean-Shift Belief Propagation (MSBP) method. Compared to existing work, our approach has multiple advantages, including: 1) incorporating higher order constraints early-on to propose highly plausible lattice points; 2) growing a lattice in multiple directions simultaneously instead of one at a time sequentially; and 3) achieving more efficient and more accurate performance than state-of-the-art algorithms. These advantages are demonstrated by quantitative experimental results on a diverse set of real world photos.

Keywords

Belief Propagation Interest Point Real Image Markov Random Field Texture Element 
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.

Supplementary material

978-3-540-88688-4_35_MOESM1_ESM.mpg (17.8 mb)
Supplementary material (18,233 KB)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Minwoo Park
    • 1
  • Robert T. Collins
    • 1
  • Yanxi Liu
    • 1
    • 2
  1. 1.Department of Computer Science and EngineeringUSA
  2. 2.Department of Electrical EngineeringThe Pennsylvania State UniversityUniversity ParkUSA

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