CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.10 - October
Issue No.10 - October (2009 vol.31)
Minwoo Park , Pennsylvania State University, University Park
Kyle Brocklehurst , Pennsylvania State University, University Park
Robert T. Collins , Pennsylvania State University, University Park
Yanxi Liu , Pennsylvania State University, University Park
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.73
We propose a novel and robust computational framework for automatic detection of deformed 2D wallpaper patterns in real-world images. The theory of 2D crystallographic groups provides a sound and natural correspondence between the underlying lattice of a deformed wallpaper pattern and a degree-4 graphical model. We start the discovery process with unsupervised clustering of interest points and voting for consistent lattice unit proposals. The proposed lattice basis vectors and pattern element contribute to the pairwise compatibility and joint compatibility (observation model) functions in a Markov Random Field (MRF). Thus, we formulate the 2D lattice detection as a spatial, multitarget tracking problem, solved within an MRF framework using a novel and efficient Mean-Shift Belief Propagation (MSBP) method. Iterative detection and growth of the deformed lattice are interleaved with regularized thin-plate spline (TPS) warping, which rectifies the current deformed lattice into a regular one to ensure stability of the MRF model in the next round of lattice recovery. We provide quantitative comparisons of our proposed method with existing algorithms on a diverse set of 261 real-world photos to demonstrate significant advances in accuracy and speed over the state of the art in automatic discovery of regularity in real images.
Belief propagation, MRF, mean shift, lattice detection, wallpaper patterns.
Minwoo Park, Kyle Brocklehurst, Robert T. Collins, Yanxi Liu, "Deformed Lattice Detection in Real-World Images Using Mean-Shift Belief Propagation", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 10, pp. 1804-1816, October 2009, doi:10.1109/TPAMI.2009.73