IS = { zkontrolovano 15 Jan 2008 },
  UPDATE  = { 2007-10-08 },
author =      {Matou{\v s}ek, Martin},
supervisor =  {Hlav{\'a}{\v c}, V{\'a}clav and {\v S}{\'a}ra, Radim},
title =       {Epipolar Rectification Minimising Image Loss},
school =      {Center for Machine Perception, K13133 FEE
               Czech Technical University},
address =     {Prague, Czech Republic},
year =        {2007},
month =       {October},
day =         {3},
type =        {PhD Thesis},
number =      {CTU--CMP--2007--06},
issn =        {1213-2365},
pages =       {119},
project =     {1M05670, MRTN-CT-2004-005439 VISIONTRAIN, FP6-IST-027113 eTRIMS},
annote = {An important task in computer vision is the automatic
  creation of 3D models of real world scenes from given sets of
  images. Dense correspondence search is one of building blocks used
  to solve this problem. To be efficient, dense matching algorithms
  rely on epipolarly rectified images in which the correspondence
  search occurs per image rows. It was observed early that it is
  almost impossible to directly acquire images in this
  configuration. Thus the images acquired in a general camera
  configuration must be transformed by epipolar rectification prior to
  their use in a dense matching algorithm.

  Recent research methods solving the epipolar rectification problem
  concentrate on two main issues: the rectification ambiguity and the
  degradations caused to the rectified images. The state-of-the-art
  rectifications aims to solve the ambiguity such that the
  degradations are as small as possible in the worst case. The
  approaches are based on some general requirements on the geometry of
  rectified images, without taking image data into account.

  The problem of projective epipolar rectification of images for dense
  stereoscopic matching is analysed in this thesis. The ambiguity of
  the projective epipolar rectification is studied in detail. It is
  shown that the equivalence class of admissible rectifications forms
  a group.

  Evaluation of image degradation caused by a geometric image
  transformation is proposed, based on an analysis of frequencies
  present in the image data. The optimal rectification transformation
  is found by minimising image frequency loss over the class of all
  admissible rectifications.},
keywords =    {computer vision, stereo, projective epipolar rectification, image loss},
psurl       = {[Matousek-PhD-TR-2007-06.pdf]},