@inproceedings{pritts-cvpr2014,
  IS = { zkontrolovano 07 Feb 2015 },
  UPDATE  = { 2014-08-05 },
author = {Pritts, James and Chum, Ond{\v r}ej and Matas, Ji{\v r}{\'\i}},
affiliation = {13133-13133-13133},
authorship = {34-33-33},
title = {Detection, Rectification and Segmentation of Coplanar Repeated Patterns},
booktitle   = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages       = {2973--2980},
book_pages  = {4304  },
isbn        = {978-1-4799-5119-2  },
publisher   = { IEEE  Computer Society },
address     = { Los Alamitos, USA },
month       = { June },
day         = { 24--27 },
Year        = {2014},
venue       = { Columbus, US },
keywords    = { rectification,repeated patterns,segmentation  },
annote      = {This paper presents a novel and general method for the
                  detection, rectification and segmentation of imaged
                  coplanar repeated patterns. The only assumption made
                  of the scene geometry is that repeated scene
                  elements are mapped to each other by planar
                  Euclidean transformations. The class of patterns
                  covered is broad and includes nearly all commonly
                  seen, planar, man-made repeated patterns. In
                  addition, novel linear constraints are used to
                  reduce geometric ambiguity between the rectified
                  imaged pattern and the scene pattern. Rectification
                  to within a similarity of the scene plane is
                  achieved from one rotated repeat, or to within a
                  similarity with a scale ambiguity along the axis of
                  symmetry from one reflected repeat. A stratum of
                  constraints is derived that gives the necessary
                  configuration of repeats for each successive level
                  of rectification. A generative model for the imaged
                  pattern is inferred and used to segment the pattern
                  with pixel accuracy. Qualitative results are shown
                  on a broad range of image types on which
                  state-of-the-art methods fail.},
Project     = {GACR P103/12/2310,ERC-CZ LL1303,GACR P103/12/G084},
}