IS = { zkontrolovano 31 Jan 2011 },
  author =       {Shekhovtsov, Alexander and Hlav{\'a}{\v c}, V{\'a}clav},
  title =        {Joint Image {GMM} and Shading {MAP} Estimation},
  c_title =      {Sou{\v c}asn{\' y} odhad obrazu (GMM) a st{\' \i}nov{\' a}n{\' \i} (MAP)},
  year =         {2010},
  month =        {August},
  day =          {23-26},
  pages =        {1360-1363},
  book_pages =   {4200},
  booktitle =    {{ICPR}'2010: Proceedings of the 20th International
                  Conference on Pattern Recognition},
  editor =       {Roy Sterritt},
  publisher =    {IEEE Computer Society},
  address =      {Los Alamitos, USA},
  isbn =         {978-0-7695-4109-9},
  issn =         {1051-4651},
  organization = {IEEE Computer Society},
  venue =        {Istanbul, Turkey},
  authorship =   {90-10},
  project =      {FP7-ICT-247870 NIFTi, FP7-ICT-247525 HUMAVIPS only EU, 1M0567},
  psurl       =  {[pdf, 883 KB] },
  acceptance_ratio = {385/2140 oral},
  keywords = {EM algorithm, color Gaussian mixture, image GMM, 
    image representation, parameter estimation, 
    piecewise-smooth gray scale shading function,
    quadratic regularization;shading MAP estimation;statistical model,
    total variation regularization;Gaussian processes, image colour analysis,
    image representation;maximum likelihood estimation},
  annote = {We consider a simple statistical model of the image,
    in which the image is represented as a sum of two parts: one part
    is explained by an i.i.d. color Gaussian mixture and the other
    part by a (piecewise-) smooth grayscale shading function. The
    smoothness is ensured by a quadratic (Tikhonov) or total variation
    regularization. We derive an EM algorithm to estimate
    simultaneously the parameters of the mixture model and the
    shading. Our algorithms for both kinds of the regularization solve
    for shading and mean parameters of the mixture model jointly.},