Deformed lattice discovery via efficient mean-shift belief propagation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
Pages474-485
Number of pages12
EditionPART 2
DOIs
StatePublished - Dec 1 2008
Event10th European Conference on Computer Vision, ECCV 2008 - Marseille, France
Duration: Oct 12 2008Oct 18 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5303 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th European Conference on Computer Vision, ECCV 2008
CountryFrance
CityMarseille
Period10/12/0810/18/08

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Park, M., Collins, R., & Liu, Y. (2008). Deformed lattice discovery via efficient mean-shift belief propagation. In Computer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings (PART 2 ed., pp. 474-485). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5303 LNCS, No. PART 2). https://doi.org/10.1007/978-3-540-88688-4-35