PatchMatch-based Automatic Lattice Detection for Near-Regular Textures

PatchMatch-based Automatic Lattice Detection for Near-Regular Textures
Title:
PatchMatch-based Automatic Lattice Detection for Near-Regular Textures
Other Titles:
2015 IEEE International Conference on Computer Vision (ICCV)
DOI:
10.1109/ICCV.2015.29
Publication Date:
07 December 2015
Citation:
S. Liu, T. T. Ng, K. Sunkavalli, M. N. Do, E. Shechtman and N. Carr, "PatchMatch-Based Automatic Lattice Detection for Near-Regular Textures," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 181-189. doi: 10.1109/ICCV.2015.29
Abstract:
In this work, we investigate the problem of automatically inferring the lattice structure of near-regular textures (NRT) in real-world images. Our technique leverages the PatchMatch algorithm for finding k-nearest-neighbor (kNN) correspondences in an image. We use these kNNs to recover an initial estimate of the 2D wallpaper basis vectors, and seed vertices of the texture lattice. We iteratively expand this lattice by solving an MRF optimization problem. We show that we can discretize the space of good solutions for the MRF using the kNNs, allowing us to efficiently and accurately optimize the MRF energy function using the Particle Belief Propagation algorithm. We demonstrate our technique on a benchmark NRT dataset containing a wide range of images with geometric and photometric variations, and show that our method clearly outperforms the state of the art in terms of both texel detection rate and texel localization score.
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Funding Info:
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(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
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