Segmentation: Region Based Methods

1
Segmentation Region Based Methods

• Region-based methods
• iterative methods based on region merging and/or
  splitting based on the degree of similarity of
  region properties (attributes).
• Region growing (narustání oblastí)
• Initialization The image is split into a large
  number of segments (regions). The initial
  segments can be even formed by individual pixels.
  
• Iteration The neighboring regions are grouped
  together if their properties (mostly intensity)
  are similar.
• Remarks
• It is wise to pose certain constrains on the
  merging process.
• The constrains can be quite complex.

2
Segmentation Region Based Methods

• Split and Merge (štepení a slucování)
• Input 1) The original image (one region).
• 2) Individual pixels (many regions).
• 3) A moderate number of regions.
• Iteration those regions that are not homogenous
  are split into several smaller regions and the
  neighboring regions that have similar properties
  are merged together.
• Remarks
• The conditions for splitting and merging can be
  quite complex.
• Often quad-tree method is used the
  non-homogenous regions are being split into 4
  equal subregions (quadrants) and 4 neighboring
  regions of similar properties are being merged
  together as far as possible. After that, region
  merging follows applied to regions in different
  pyramid levels or having a different parent.

3
Segmentation Region Based Methods

• Quad-tree method

source Sonka, Hlavac, Image Processing, Analysis
and Machine Vision
4
Segmentation Region Based Methods

• Quad-tree method examples

source http//verona.fi-p.unam.mx/project/quadtre
e.htm
5
Texture Segmentation

• Texture segmentation
• dividing the image into regions based on the
  texture characteristics.
• Method 1 (Rosenfeld et al.)
• Idea Texture measure is defined and computed for
  all pixels (within a certain neighborhood of the
  given pixel). Thus, texture is converted to
  amplitude and then one of the amplitude
  segmentation methods is applied.
• Drawback texture boundaries are blurred.
• Method 2 (Thompson)
• Idea The transitions between regions of
  differing texture are detected. Thus, an edge map
  is built. Then the edge map is processed
  similarly to the edge-based segmentation.
• Drawback edges are not continuous.

6
Texture Segmentation

• Examples of different textures
• Curtain Dog fur
• Clothes 1 Clothes 2

7
Texture Segmentation

• Examples of texture segmentation

source http//debut.cis.nctu.edu.tw/pages/Texture
Study/segmentation.htm
8
Texture Segmentation

• Different scales of the texture in the curtain
  image
• Individual threads Meshes of the
  net Folds of the curtain
• Consequence of this example
• If the image contains textures of different
  scales, it is necessary to perform multiscale
  texture analysis, i.e. to analyze textures in a
  hierarchical manner (at different scales).

9
Segmentation Clustering Methods

• Clustering methods
• Methods based on cluster analysis.
• Idea At each pixel of the image a vector x
  x1 ,...,xN of N different measurements is
  computed (typically N is about 10). Different
  user-defined characteristics are measured. Then
  cluster analysis in the N-dimensional space is
  performed, i.e. those pixels which form a cluster
  in the N-dimensional space are segmented into one
  region.
• Advantage easy.
• Drawback computationally intensive.

10
Segmentation Template Matching

• Template matching (srovnávání se vzorem)
• Searching for a template (pattern) in the image
  by computing the correlation between the pattern
  and the searched image data.
• Idea
• Evaluate a match criterion for each location and
  rotation of the pattern in the image.
• Local maxima of this criterion exceeding a preset
  threshold represent pattern locations in the
  image.
• Disadvantages
• Very time consuming, especially for large
  patterns.
• Sensitive to geometrical distortions of the image.

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Segmentation Template Matching

• Possible matching criteria
• f(x,y) image
• h(x,y) pattern
• S set of all possible pixel coordinates in the
  image h