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.

Text Reference

Minwoo Park, Robert T. Collins and Yanxi Liu, "Deformed Lattice Detection via Mean-Shift Belief Propagation," European Conference on Computer Vision (ECCV), October, 2008

BibTeX Reference

@inproceedings{Park_ECCV_2008,
    author = "Minwoo Park, Robert T. Collins and Yanxi Liu",
    title = "Deformed Lattice Detection via Mean-Shift Belief Propagation",
    booktitle = "European Conference on Computer Vision (ECCV)",
    month = "October",
    year = "2008",
}