cvrDocuTransformationEstimation.h

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/*----------------------------------------------------------------
 * project ....: LTI Digital Image/Signal Processing Library
 * file .......: cvrCsPresegmentation.h
 * authors ....: Pablo Alvarado
 * organization: LTI, RWTH Aachen
 * creation ...: 8.11.2001
 * revisions ..: $Id: cvrDocuSegmentation.h,v 1.3 2006/01/03 19:56:00 alvarado Exp $
 */

#ifndef _CVR_DOCU_TRANSFORMATION_ESTIMATION
#define _CVR_DOCU_TRANSFORMATION_ESTIMATION

/**
  \page transEstPage Overview for estimation of geometric transformations

  The estimation of a geometric transformation between two images is a complex
  task with many steps involved.  This page intents to provide a general
  overlook to all concepts related with the task, and the classes of the
  library used for this matter.

  \section stepSect Steps involved

  The steps involved are usually the following
  -# Computation of locations, i.e. of conspicuous places in both images.
     This can be done for instance with the cvr::fastHessianDetection.
  -# Computation of descriptors for the locations detected.  Several descritor
     extractors can be used and their suitability for each application will be
     decisive in the selection of the proper class.  See for instance
     cvr::surfLocalDescriptor.
  -# Finding the correspondences between the locations of both images.  The
     success of the estimation depends on finding which points in a first image
     correspond to which points in the second image.  For this task the
     "similarity" between the locations is computed by means of distances of
     the descriptors. See cvr::locationMatch for this task
  -# The estimation of the transformation

  \section estimSect Estimation of the transformation

  The estimation is a complex task and still a hot research topic.  You can
  conceive the process in two levels: a robust estimator, whose goal is to
  detect and eliminate the "outliers" in the list of correspondences.  The
  robust estimator, which can be cvr::ransacEstimation or cvr::prosacEstimation
  in turn use a "low-level" transformation estimation.

  The library provide several classes for the low-level estimations of planar
  transformations (2D) or spatial transformation (3D):
  - Euclidean transformations: just rotations and translations (without
    scaling).  See cvr::euclideanTransformation2D,
    cvr::euclideanTransformation3D
  - Similarity transformations: rotations, translations and isotropic scaling.
    See cvr::similarityTransformation2D, cvr::similarityTransformation3D
  - Affine transformation: rotations, translations and anisotropic scalings.
    See cvr::affineTransformation2D, cvr::affineTransformation3D
  - Projective transformation: free invertible linear transformations.
    See cvr::projectiveTransformation2D, cvr::projectiveTransformation3D
  - Fundamental matrix estimation.  This is used in the reconstruction of
    3D coordinates of points found in at least two 2D images.
*/

#endif