INFO-H-500 Image Acquisition & Processing
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  • 1.Introduction
    • Biological vision
    • Image sensors
    • Image representation
  • 2.Low-level image processing
    • Histogram
    • Linear filtering
    • Rank based filters
    • Image restoration
    • Edge detection
  • 3.Image segmentation
    • Typical image processing pipelines
    • Histogram based segmentation
    • Border based segmentation
    • Region based segmentation
    • Model based segmentation
      • Model based segmentation
      • Active contours
    • Examples
  • 4.Detection
    • Hough transform
    • Detectors
  • 5.Morphomathematics
    • Morphomathematical operators
    • Combined operations
    • The watershed transform
    • Gray level morphology
  • 6.Objects features
    • Statistical features
    • Contour features
    • Object moments
    • Texture features
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  • Region based segmentation
    • Region growing
    • Split and merge
    • the watershed transform
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%matplotlib inline
from IPython.display import HTML,Image,SVG,YouTubeVideo
%matplotlib inline from IPython.display import HTML,Image,SVG,YouTubeVideo

Region based segmentation¶

Because edge based segmentation leave contours not closed, one may be interested in searching for regions.

Region growing¶

Starting from one point of the “object” region and recruiting neighbouring pixels.

Split and merge¶

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Image('../data/quadtree.png')
Image('../data/quadtree.png')
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split phase¶

one define an homogeneity function

recursive image split:

  • if image is homogeneous
    • return
  • else
    • split the image into 4 and apply the test on each sub-image

merge phase¶

  • for each adjacent sub-image
    • if the two grouped regions satisfies the homogeneity criterion, merge the regions
In [3]:
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Image('../data/split_example.png')
Image('../data/split_example.png')
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see also:

  • Split and merge DIP p.461

the watershed transform¶

see also:

  • watershed in the mathematical morphology section
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