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Parallelization of RHSEG

Parallelization of RHSEG. - Mahmood Vinayak. Agenda. Problem Related work Algorithm in detail Experiment & Results Future Work. Problem description. Background:

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Parallelization of RHSEG

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  1. Parallelization of RHSEG - Mahmood Vinayak

  2. Agenda • Problem • Related work • Algorithm in detail • Experiment & Results • Future Work

  3. Problem description Background: Image segmentation is the partitioning of an image into related sections or regions. Segmentation is a key first step for a number of approaches to image analysis and compression. Segmentation: A segmentation hierarchy is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. Why? Change/simplify the representation of an image to more meaningful format easier to analyze

  4. Related Work • HSWO: Hierarchical step-wise optimization • Region growing segmentation • Compares(merge) only spatially adjacent regions HSWO • HSEG: Hierarchical segmentation • Augmented HSWO • Merges spatially non-adjacent regions • Computational intensive HSEG • RHSEG: Recursive Hierarchical segmentation • Augmented HSEG • Merges spatially non-adjacent regions • Reasonable number of comparisons (merges) RHSEG

  5. Parallelization

  6. Algorithm 1 2 3 4

  7. Algorithm 2 2 1 1 2 3 1 4 4 5 6 3 1 2 3 4 5 6 7 8 9 7 8 9 1 2 3 1 2 3 4 5 6 4 5 6 3 4 7 8 9 7 8 9

  8. Algorithm 1 2 3 1 2 3 1 2 3 4 1 6 4 5 6 4 5 6 7 8 9 7 8 9 7 8 9 1 2 3 1 2 3 4 1 6 2 1 6 7 8 9 7 8 9

  9. Algorithm 10 7 4 1 1 2 2 8 11 2 5 2 1 3 6 9 3 12 1 2 3

  10. Experiments and results Assumptions : Gray scale images.

  11. Experiments and results Apply experiments on : • Images with different sizes.. -40 x 40 -160 X 160 -640 X 640 -1280 X 1280 • Different # of processors • Different recursive levels. • Different number of regions.

  12. Experiments and results Different number of regions:

  13. Experiments and results C = 10 C = 5 The original image C = 15 C = 20

  14. Experiments and results Time C = 10 C = 5 C C = 15 C = 20

  15. Experiments and results Different recursive levels :

  16. Experiments and results LR = 8 LR = 7 LR = 6 Time LR

  17. Experiments and results Different number processors : Time Speed up # of processors # of processors

  18. Experiments and results Different number processors : Time Speed up # of processors # of processors

  19. Future work • Use non-blocking version for send and receive to hide the latency. • Apply the same algorithm on RGB images, and hyper- spectral images.

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