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S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama University of Waterloo, Ontario, Canada

Optimization of Object Extraction Based on One User-Prepared Sample. S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama University of Waterloo, Ontario, Canada MOPTA, Windsor, July 26, 2005. Agenda. Objective Proposed Approach Preliminary Results Comparison with Other Methods Conclusion

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S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama University of Waterloo, Ontario, Canada

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  1. Optimization of Object Extraction Based on One User-Prepared Sample S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama University of Waterloo, Ontario, Canada MOPTA, Windsor, July 26, 2005

  2. Agenda • Objective • Proposed Approach • Preliminary Results • Comparison with Other Methods • Conclusion • Future Works

  3. Main Objective Acquisition of object extraction procedure from user-prepared sample(s) based on genetic optimization of morphological processing chains

  4. Reasons for Developing Automated Image Processing Systems Dealing with huge number of images  Saving experts valuable time  Possibility of using in online applications  Overcoming of inconsistent nature of human processing  Supporting required high accuracy

  5. Why Learning from a Small Number of Samples is Valuable ? Because : It reduces the expected level of expert participation which is the main obstacle for research and development. Preparing some manually generated samples to reflect the experts’ expectations is a reasonable requirement in all image processing environments.

  6. Proposed Approach • Utilizing Mathematical morphology operations, as image processing tools, to build object extraction procedure • Using genetic algorithm, as optimizer tools, to find optimal parameters of above mentioned procedure

  7. Morphological Operations They are shape-based operations  Used to handle a wide range of image processing tasks, ranging from noise filtering to object extraction

  8. Genetic Optimizer Optimal Ordering of Operations Parameter & Ordering Optimizer Procedure Applier Input Image Optimal Parameters Mathematical Morphology Operations Input Images Result images Gold Image Main Structure of Proposed Approach

  9. Morphological Operations Chain as a Morphological Procedure • K3*{O(SE1)_C(SE2)}K1*E(SE3)K2*D(SE4) • 2. K1*E(SE3)K3*{O(SE1)_C(SE2)}K2*D(SE4) • 3. K1*E(SE3)K2*D(SE4)K3*{O(SE1)_C(SE2)} • 4. K3*{O(SE1)_C(SE2)}K2*D(SE4)K1*E(SE3) • 5. K3*{O(SE1)_C(SE2)}K2*D(SE4)K1*E(SE3) • 6. K3*{O(SE1)_C(SE2)}K2*D(SE4)K1*D(SE3) SE1, SE2, SE3, and SE4: Corresponding structural elements K1, K2, and K3 : Iteration times for operations O: Opening C: Closing D: Dilation E: Erosion

  10. Start Genetic Optimization of MM Procedure Population Initialization Applying MM Procedure Computing of Dissimilarity Is Reached Ending Criteria? End Yes No Selection Crossover Mutation

  11. Preliminary Results • Circle Extraction • Triangle Extraction • Rectangle Extraction • Object Extraction Applied for Grey-level Images

  12. Utilized Measures Matching Index: Overall Matching Index:

  13. Training for Object Extraction- Circle • Original image • Goal image • Generated image by MM procedure (94.48% similarity)

  14. Improvement of Result Performance During Training

  15. Object (Circle) Extraction Training Results

  16. Verification of Optimization

  17. Results of Object (Circle) Extraction

  18. Training for Object Extraction- Triangle • Original image • Goal image • Generated image by MM procedure (85.01% similarity)

  19. Results of Object (Triangle) Extraction

  20. Training for Object Extraction- Rectangle • Original image • Goal image • Generated image by MM procedure (94.37% similarity)

  21. Results of Object (Rectangle) Extraction

  22. Summary of Numerical Results

  23. Object Extraction Applied on Gray-scale Images • Grey scale image • Goal image • Generated image by MM procedure (76.77% similarity)

  24. Some Results of Object Extraction in Grey Level Images 95.05% 96.05% 96.09% 96.71% Overall matching rate: 95.90% with standard deviation of 0.54% 95.63%

  25. Level of supported variations

  26. Training for Fully Rotation Invariant Triangle Extraction 1 1 Genetic Optimizer 2 2 Input Images Result Images 3 3 4 4 2 1 3 4 Gold Images

  27. Comparing Proposed Approach with Knowledge-Based Learning  Knowledge acquisition difficulties √  Unable of self-learning √  Difficult to avoid conflicts in large knowledge bases √  Knowledge reliability problem √ √ : Proposed approach solves it mostly or it is not applicable.

  28. Comparing Proposed Approach with Sample-Based (NN) Learning  Sample providing problem √  Problem of choosing the best architecture √ ~: Proposed approach solves it partially.

  29. Conclusion The outstanding features of the proposed approach are as follows: - Training based on a few samples • Supporting (semi) automated image processing • Mostly invariant for noising, overlapping, translation, rotating, scaling, and duplicating.

  30. Future Works - Extending functionality of the system to cover wider range of image processing tasks - Applying on medical image processing

  31. Thank you for your attention and patience.

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