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Chapter 10. Image Segmentation. Preview. Segmentation subdivides an image into its constituent regions or objects. Level of division depends on the problem being solved.

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chapter 10

Chapter 10

Image Segmentation

preview
Preview
  • Segmentation subdivides an image into its constituent regions or objects.
  • Level of division depends on the problem being solved.
  • Image segmentation algorithms generally are based on one of two basic properties of intensity values: discontinuity (e.g. edges) and similarity (e.g., thresholding, region growing, region splitting and merging)
chapter outline
Chapter Outline
  • Detection of discontinuities
  • Edge linking and boundary detection
  • Thresholding
  • Region-based segmentation
  • Morphological watersheds
  • Motion in segmentation
detection of discontinuities
Detection of Discontinuities
  • Define the response of the mask:
  • Point detection:
line detection
Line Detection
  • Masks that extract lines of different directions.
edge detection
Edge Detection
  • An ideal edge has the properties of the model shown to the right:
  • A set of connected pixels, each of which is located at an orthogonal step transition ingray level.
  • Edge: local concept
  • Region Boundary: global idea
ramp digital edge
Ramp Digital Edge
  • In practice, optics, sampling and other image acquisition imperfections yield edges that area blurred.
  • Slope of the ramp determined by the degree of blurring.
edge point
Edge Point
  • We define a point in an image as being an edge point if its 2-D 1st order derivative is greater than a specified threshold.
  • A set of such points that are connected according to a predefined criterion of connectedness is by definition an edge.
gradient operators
Gradient Operators
  • Gradient:
  • Magnitude:
  • Direction:
the laplacian
The Laplacian
  • Definition:
  • Generally not used in its original form due to sensitivity to noise.
  • Role of Laplacian in segmentation:
    • Zero-crossings
    • Tell whether a pixel is on the dark or light side of an edge.
edge linking local processing
Edge Linking: Local Processing
  • Link edges points with similar gradient magnitude and direction.
global processing hough transform
Global Processing: Hough Transform
  • Representation of lines in parametric space: Cartesian coordinate
hough transform
Hough Transform
  • Representation in parametric space: polar coordinate
thresholding
Thresholding
  • Foundation: background point vs. object point
  • The role of illumination: f(x,y)=i(x,y)*r(x,y)
  • Basic global thresholding
  • Adaptive thresholding
  • Optimal global and adaptive thresholding
  • Use of boundary characteristics for histogram improvement and local thresholding
  • Thresholds based on several variables
optimal global and adaptive thresholding
Optimal Global and Adaptive Thresholding
  • Refer to Chapter 2 of the “Pattern Classification” textbook by Duda, Hart and Stork.
region based segmentation
Region-Based Segmentation
  • Let R represent the entire image region. We may view segmentation as a process that partitions R into n sub-regions R1, R2, …, Rn such that:
    • (a)
    • (b) Ri is a connected region
    • (c)
    • (d) P(Ri)= TRUE for i=1,2,…n
    • (e) P(Ri U Rj)= FALSE for i != j
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