cvrDocuSegmentation.h

/*
 * Copyright (C) 1998-2006
 * Lehrstuhl fuer Technische Informatik, RWTH-Aachen, Germany
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/**
 * \file cvrCsPresegmentation.h
 * \author Pablo Alvarado
 * \date 8.11.2001
 * $Id: cvrDocuSegmentation.h,v 1.3 2006/01/03 19:56:00 alvarado Exp $
 */

#ifndef _CVR_DOCU_SEGMENTATION
#define _CVR_DOCU_SEGMENTATION

/**
  \page segmPage Segmentation Overview

  Segmentation is a complex task, and the number of functors in the CVR-Lib
  that deal with segmentation grows...  A few extra-notes are required.

  There are two sorts of segmentation functors.  Traditional
  "complete" algorithms and "blocks", with which you can build your
  own segmentation approach.  There are also tools to convert the
  results from one representation to another.

  \section completeSeg Complete Algorithms

  The complete algorithms are:

  - Threshold segmentation.  Used to separate background from object
    (two-classes segmentation).  You give a "window" for pixel values that
    should be recognized as object.  The rest will be background.  Some
    algorithms can choose automatically the threshold values.
    (see cvr::thresholdSegmentation, cvr::optimalThresholding)
  - Watershed segmentation.  This sort of algorithms are widely used.  Several
    implementations can be chosen using the functor parameters
    (see cvr::watershedSegmentation)
  - Comaniciu's Mean-Shift algorithm is also implemented.  With it you
    can choose between quantization, oversegmentation and undersegmentation.
    (see cvr::meanShiftSegmentation)
  - Region growing for a two-classes segmentation problem can also be found
    here (see cvr::regionGrowing)

  \section blockSeg Functional Blocks

  There are several blocks, that can be used as part of a more complex
  segmentation approach.

  - A statistical background model per pixel can detect a continuous background
    from a moving object or objects.
    (see cvr::backgroundModel)
  - A pre-segmentation approach is implemented, which finds regions
    using color quantization and marks as background the colors most
    used at the border in cvr::csPresegmentation.
  - Similar to the pre-segmentation is the cvr::kMeansSegmentation, but here
    no background is identified, only the color quantization and (optionally)
    the smoothing stages are done.
  - cvr::whiteningSegmentation uses the kMeansSegmentation to segment the
    original image and a transformed one, and combines the results of both.
  - Given a list of area points, where each object (or region) is represented
    by an cvr::areaPoints %object, it is possible to generate a so called
    similarity matrix using the cvr::similarityMatrix functor.  Here a
    similarity measure will be generated for all adjacent objects.
  - With a similarity matrix it is possible to merge similar regions using
    the cvr::regionMerge %functor.

  \section toolsSeg Tools for segmentation

  Segmentation algorithms produce usually so called "masks",
  i.e. channels, where each pixel contains a label value.  These masks
  can be considered for two classes problems, where only values of
  zero and not-zero will be differentiated, and "labeled" masks, where
  each value is considered as a different object.  To extract the
  position of each single object in the image in an efficient way the
  functor \b cvr::objectsFromMask should be used.  Sometimes the
  \b cvr::fastRelabeling %functor provides all functionality required for
  this task.

  The cvr::boundingBox functor can use one of the pointLists extracted with
  cvr::objectsFromMask to produce a new image with only one object.

  It is useful sometimes to generate a color %image, where each label
  found in the %segmentation is replaced by its mean color, producing
  an color quantized %image.  Here, the functors cvr::usePalette and
  cvr::computePalette are helpful.
*/

#endif