@Article{Kukelova-Bujnak-Pajdla-PAMI-2012,
  IS = { zkontrolovano 10 Oct 2012 },
  UPDATE  = { 2012-10-10 },
  author = {Kukelova, Zuzana and Bujnak, Martin and Pajdla, Tom{\' a}{\v s}},
  title = {Polynomial Eigenvalue Solutions to Minimal Problems in Computer Vision},
  journal = {{IEEE} Transactions on Pattern Analysis and Machine Intelligence},
  year = {2012},
  volume = {34},
  pages = {1381-1393},
  number = {7},
  month = {July},
  address = {Los Alamitos, USA},
  annote = {We present a method for solving systems of polynomial
    equations appearing in computer vision. This method is based on
    polynomial eigenvalue solvers and is more straightforward and
    easier to implement than the state-of-the-art Grobner basis method
    since eigenvalue problems are well studied, easy to understand,
    and efficient and robust algorithms for solving these problems are
    available. We provide a characterization of problems that can be
    efficiently solved as polynomial eigenvalue problems (PEPs) and
    present a resultant-based method for transforming a system of
    polynomial equations to a polynomial eigenvalue problem.  We
    propose techniques that can be used to reduce the size of the
    computed polynomial eigenvalue problems. To show the applicability
    of the proposed polynomial eigenvalue method, we present the
    polynomial eigenvalue solutions to several important minimal
    relative pose problems.},
  authorship = {34-33-33},
  issn = {0162-8828},
  keywords = {Structure from motion, relative camera pose, 
    minimal problems, polynomial genvalue problems.},
  project = {FP7-SPACE-241523 PRoViScout, MSM6840770038},
  psurl = {http://dx.doi.org/10.1109/TPAMI.2011.230},
  publisher = {IEEE Computer Society},
  ut_isi = {000304138300010},
}