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Using Evolutionary Algorithm to find image segmentation Yossef Kitrossky & Yoad Lewenberg Evolutionary Algorithm First Generation Population Evolutionary Algorithm First Generation Population Individual Individual B A Evolutionary

  1. Using Evolutionary Algorithm to find image segmentation Yossef Kitrossky & Yoad Lewenberg

  2. Evolutionary Algorithm First Generation Population

  3. Evolutionary Algorithm First Generation Population Individual Individual B A

  4. Evolutionary Algorithm First Generation Population Individual Individual B A Individual C

  5. Evolutionary Algorithm First Generation Population Individual Individual B A Individual C Individual C’

  6. Evolutionary Algorithm First Generation Population Individual Individual B A Individual C Individual C’ New Population

  7. First Generation • Random Matrix • Circles and rectangle

  8. First Generation • Random Matrix • Circles and rectangles

  9. First Generation • Random Matrix • Circles and rectangle

  10. First Generation • Random Matrix • Circles and rectangle Mutation probability 0.02 Mutation probability 0.2

  11. First Generation • Random Matrix • Circles and rectangles Mutation probability 0. 2 Mutation probability 0.02

  12. Evolution  Reducing image resolution 64*64 128*128 32*32 16*16 8*8

  13. Evolution

  14. Evolution 20 generation of evaluation according to 8*8 resized image

  15. Evolution 40 generation of evaluation according to 16*16 resized image

  16. Evolution 80 generation of evaluation according to 32*32 resized image

  17. Evolution 100 generation of evaluation according to 64*64 resized image

  18. Evolution 160 generation of evaluation according to original image

  19. Evolution

  20. Selection  The best 10% individuals join to the next generation as they are.  For the last 90%:  Randomly choose 4 individuals.  The best one chosen as parent A.  In the same way parent B is chosen.  The offspring of A and B, be a member of the next generation.

  21. Merge  Randomly choose pivot  Randomly choose axis  With some probability mutate the result

  22. Merge  Randomly choose pivot  Randomly choose axis  With some probability mutate the result

  23. Merge  Randomly choose pivot  Randomly choose axis  With some probability mutate the result Pivot = 54, y axis

  24. Mutation Method 1  Flip random index  Method 2  Add circle  Add rectangle  Smooth  Segment expansion 

  25. Mutation Method 1  Flip random index  Method 2  Add circle  Add rectangle  Smooth  Segment expansion 

  26. Mutation Method 1  Flip random index  Method 2  Add circle  Add rectangle  Smooth  Segment expansion 

  27. Mutation Method 1  Flip random index  Method 2  Add circle  Add rectangle  Smooth  Segment expansion 

  28. Mutation Method 1  Flip random index  Method 2  Add circle  Add rectangle  Smooth  Segment expansion 

  29. Fitness Function  I= 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4

  30. Fitness Function  I= 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 z 4 4 4 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4

  31. Fitness Function  I= 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4 2 2 2 4 4 4 4 4

  32. Fitness Function  A= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1

  33. Fitness Function  Low variance in each segment.  High derivative at boundary points

  34. Fitness  A= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 At boundary point by x axis, should receive high values

  35. Fitness  I x = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0

  36. Fitness  A= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 At boundary point by y axis, should receive high values

  37. Fitness Function  I y = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 -1 -1 -1 0 0 0 -1 -1 -1 -1 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

  38. Fitness

  39. Image with noise

  40. Image with noise

  41. Running time  For n*n image:  Creating the initial population.  For every generation;  Ranking all the population  for every individual;  Pick parents  Merge  Mutate  Total running time: -

  42. Running time  For n*n image:  Creating the initial population.  For every generation;  Ranking all the population  for every individual;  Pick parents  Merge  Mutate  Total running time:

  43. Running time  For n*n image:  Creating the initial population.  For every generation;  Ranking all the population  for every individual;  Pick parents  Merge  Mutate  Total running time:

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