Dip Image Segmentation

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Dip Image Segmentation

  1. 1. Image Segmentation Chapter 10
  2. 2. Image Segmentation Segmentation subdivides an image into its constituent regions or groups. The level to which the subdivision is carried depends on the problem being solved. That is, segmentation should stop when the objects of interest in an application have been isolated. e.g. automated inspection of electronic assemblies; specific anomalies; missing components or broken connection paths.
  3. 3. Image Segmentation Segmentation algorithms ; Two categories based on two basic properties of intensity values : discontinuity and similarity First Category : Abrupt changes in intensity ; edges Second Category : partitionning of regions which are similar according to a set of predefined criteria. e.g. Thresholding, region growing, region splitting and merging.
  4. 4. Image Segmentation First Category : Points, Lines, Edges
  5. 5. Detection of discontinuities Points, lines, edges The most common way R = w1*z1 + w2*z2 + ……+ w9*z9
  6. 6. Point detection  R   T T = Threshold Figure 10.2 (a) point detection mask
  7. 7. Point detection (b) X-ray image of a turbine blade with porosity (c) Result of point detection mask (d) Result of point detection mask with threshold Figure 10.2
  8. 8. Line detection – A Suitable Mask in desired direction – Thresholding Figure 10.3 Line masks
  9. 9. • Example: Line detection -45º Mask Thresholding Figure 10.4 Illustration of line detection (a) ,(b),(c)
  10. 10. Edge Detection – Two Mathematical model
  11. 11. Edge Detection Second derivative First derivative Gray level profile
  12. 12. Problem of Noise Gaussian Noise (mean, sigma)
  13. 13. Gradient Operators
  14. 14. Gradient Operators X-direction Y-direction
  15. 15. – Roberts Cross Gradients: Gradient Operators – Prewitt Operators:
  16. 16. Diagonal Edge – 45-Direction 45-Direction
  17. 17. Gradient Operators
  18. 18. Gradient Operators Pre- Smoothing 5×5
  19. 19. Diagonal edge detection
  20. 20. Laplacian as an isotropic Detector: Discrete Implementation:
  21. 21. Laplacian of Gaussian (LoG):
  22. 22. Edge detection (overview)
  23. 23. Image Segmentation Second Category : Thresholding, region growing, region splitting and merging
  24. 24. Thresholding – F ( x,y )> T then ( x,y ) is belong to object , else ( x,y ) is belong to background . • Bi-level (T) • Multi-level (T1,T2,…, Tn) • Threshold image: – Threshold Estimation : • Histogram
  25. 25. Thresholding

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