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JACIII Vol.13 No.2 pp. 115-127
doi: 10.20965/jaciii.2009.p0115
(2009)

Paper:

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Automatic Acquisition of Image Filtering and Object Extraction Procedures from Ground-Truth Samples

Shahryar Rahnamayan*, Hamid R. Tizhoosh**,
and Magdy M.A. Salama**

*Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, Ontario, L1H 7K4, Canada

**Faculty of Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada

Received:
August 2, 2008
Accepted:
November 18, 2008
Published:
March 20, 2009
Creative Commons License
Keywords:
image filtering, object extraction, genetic algorithm, mathematical morphology, image processing chain
Abstract
Knowledge- and sample-based learning approaches play a pivotal role in image processing. However, the acquisition and integration of expert knowledge (for the former) and providing a sufficiently large number of training samples (for the latter) are generally hard to perform and time-consuming tasks. Hence, learning image processing tasks from a few gold/ground-truth samples, prepared by the user, is highly desirable. This paper demonstrates how the combination of an optimizer (e.g., genetic algorithm) and image processing tools (e.g., parameterized morphology operations) can be used to generate image processing procedures for image filtering and object extraction. For this purpose, the approach receives the original and the user-prepared image (filtered image or image with extracted target object) as a gold sample which reflects the user's expectations. After carrying out the training or optimization phase, the optimal procedure is generated and ready to be applied to new images. The feasibility of our approach is investigated for two individual image processing categories, namely filtering and object extraction, by well-prepared synthetic images. The proposed architecture and the employed methodologies are explained in detail. Experimental results are provided as well.

The subject matter in this work is covered by a US provisional patent application.
Cite this article as:
S. Rahnamayan, H. Tizhoosh, and M. Salama, “Automatic Acquisition of Image Filtering and Object Extraction Procedures from Ground-Truth Samples,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.2, pp. 115-127, 2009.
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References
  1. [1] G. Hinton and T.J. Sejnowski, “Unsupervised Learning- Foundations of Neural Computation,” MIT Press, 1999.
  2. [2] R. D. Reed and R. J. Marks II, “Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks,” MIT Press, Cambridge, 1999.
  3. [3] A. Gammerman (Ed.), “Computational Learning and Probabilistic Reasoning,” John Wiley & Sons, 1996.
  4. [4] D. Graham and A. Barrett, “Knowledge-based image processing systems,” Applied Computing, Springer-Verlag, pp. 41-54, UK, 1997.
  5. [5] Carl G. Looney, “Fuzzy and rule-based image convolution,” Mathematics and Computers in Simulation 51, pp. 209-219, 2000.
  6. [6] K. Arakawa, “Fuzzy rule-based image processing,” Series Studies in Fuzziness and Soft Computing, Vol.52, 2000.
  7. [7] A.S. Vu, “A computer vision system for automatic knowledge-based configuration of the image processing and hierarchical object recognition,” 10th Int. Conf. on Image Analysis and Processing, pp. 636-641, 1999.
  8. [8] F. Rossant and I. Bloch, “A fuzzy model for optical recognition of musical scores,” Fuzzy Sets and Systems 141, pp. 165-201, 2004.
  9. [9] I. Aleksander and H. Morton, “An Introduction to Neural Computing,” Thomson Computing Press, 1995.
  10. [10] M. Egmont-Petersen, D. de Ridder, and H. Handels, “Image processing with neural networks - a review,” Pattern Recognition 35, pp. 2279-2301, 2002.
  11. [11] M. Shokri and H.R. Tizhoosh, “Using reinforcement learning for image thresholding,” Canadian Conf. on Electrical and Computer Engineering, IEEE CCECE 2003, Vol.2, pp. 1231-1234, 2003.
  12. [12] D.N. Chun and H.S. Yang, “Robust image segmentation using genetic algorithm with a fuzzy measure,” Pattern Recognition, Vol.29, No.7, pp. 1195-1211, 1996.
  13. [13] F. Rossant, “A global method for music symbol recognition in typeset music sheets,” Pattern Recognition Letters 23, pp. 1129-1141, 2002.
  14. [14] K. Miyamoto, M. Tamagawa, I. Fujita, Y. Hayama, and S. Eiho, “Extraction of character string region by a correlation method,” Systems and Computers in Japan, Vol.30, No.14, 1999.
  15. [15] Y. Li, X.-L. Qi, and Y.-J. Wang, “Eye detection by using fuzzy template matching and feature-parameter-based judgment,” Pattern Recognition Letters 22, pp. 1111-1124, 2001.
  16. [16] K. Topfer and R. Jacobson, “The relationship between objective and subjective image quality criteria,” J. Information Recording Material, Vol.21, pp. 5-7, 1993.
  17. [17] R. Jacobson, “An evaluation of image quality metrics,” J. Photog. Sci., Vol.43, pp. 7-6, 1995.
  18. [18] M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantative performance evaluation,” Journal of Electronic Imaging 13(1), pp. 146-165, Jan. 2004.
  19. [19] J. Serra and P. Soille (Eds.), “Mathematical Morphology and Its applications to Image Processing,” Kluwer Academic Publishers, Dordrecht, 1994.
  20. [20] J. Holland, “Adaptation in Natural and Artificial Systems,” University of Michigan Press, Ann Arbor, MI, 1975; MIT Press, Cambridge, MA, 1992.
  21. [21] M. Gen and R. Cheng, “Genetic Algorithms & Engineering Optimization,” New York, John Wiley & Sons, INC. , 2000, ISBN: 0-471-31531-1.
  22. [22] D. E. Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning,” USA, Addison-Wesley Longman Publishing Co., 2005, ISBN:0-201-15767-5.
  23. [23] R. L. Haupt and S. E. Haupt, “Practical Genetic Algorithms,” Hoboken, New Jersey, John Wiley & Sons, INC., 2nd ed., 2004, ISBN: 0-471-45565-2.
  24. [24] M. Mitchell, “An introduction to genetic algorithms,” USA, MIT Press Cambridge, MA, 1996, ISBN: 0-262-13316-4.
  25. [25] H. Joo, R.M. Haralick, and L.G. Shapiro, “Toward the automatic generation of mathematical morphology procedures using predicate logic,” Proc. of the Int. Conf. on Computer Vision, Japan, 156-165, 1990.
  26. [26] J. Hasegawa, H. Kubota, and J. Toriwaki, “Automated construction of image procedures by sample-figure presentation,” Proc. of 8th Int. Conf. of Pat. Recog., Paris, France, pp. 586-588, 1986.
  27. [27] I. Yoda, K. Yamamoto, and H. Yamada, “Automatic acquisation of hierical mathematical morphology procedures by genetic algorithms,” Image and Vision Computing 17, pp. 749-760, 1999.
  28. [28] N. R. Harvey and S. Marshall, “The use of genetic algorithms in morphological filter design,” Signal Processing: Image Comminication 8, pp. 55-71, 1996.
  29. [29] M. S. Hamid, N. R. Harvey, and S. Marshall, “Genetic Algorithm Optimization of Multidimensional Grayscale Soft Morphological Filters With Applications in Film Archive Restoration,” IEEE Transaction on Circuits and Systems for Video Technology, Vol.13, No.5, pp. 406-416, May 2003.
  30. [30] A. Asano, Y. Kobayashi, C. Muraki, and M. Muneyasu, “Optimization of gray scale morphological opening for noise removal in texture images,” Midwest Symposium on Circuits and Systems, Vol.1, The 2004 47th Midwest Symposium on Circuits and Systems - Conf. Proc., 2004, pp. 313-316.
  31. [31] N.R. Harvey and S. Marshall, “Video and film restoration using mathematical morphology,” IEE Colloquium (Digest), No.284, 1998.
  32. [32] P. Kraft, N. R. Harvey, and S. Marshall, “Parallel genetic algorithms in the optimization of morphological filters: A general design tool,” Journal of Electronic Imaging, Vol.6, No.4, October, pp. 504-515, 1997.
  33. [33] N. R. Harvey and S. Marshall, “Use of genetic algorithms in morphological filter design,” Signal Processing: Image Communication, Vol.8, No.1, pp. 485-490, Jan, 1996.
  34. [34] M. A. Zmuda, L. A. Tamburino, and M. M. Rizki, “An Evolutionary Learning System For Synthesizing Complex Morphological Filters,” IEEE Transactions on Systems, Man, and Cybernetics-PART B: Cybernetics, Vol.26, No.4, pp. 645-653, Aug. 1996.
  35. [35] S. Rahnamayan, H.R. Tizhoosh, and M.M.A. Salama, “Learning Image Filtering from a Gold Sample Based on Genetic Optimization of Morphological Processing,” Proc. of 7th Int. Conf. on Adaptive and Natural Computing Algorithms, Springer-Verlag (Vienna), Coimbra, Portugal, pp. 478-481, 2005.
  36. [36] S. Rahnamayan, H.R. Tizhoosh, and M.M.A. Salama, “Automated Snake Initialization for the Segmentation of the Prostate in Ultrasound Images,” Int. Conf. on Image Analysis and Recognition-ICIAR2005, Springer Lecture Notes in Computer Science series (Springer LCNS), Toronto, Canada, pp. 930-937, Sep. 2005.
  37. [37] J. Goutsias, L. Vincent, and Dan S. Bloomberg (Eds.), “Mathematical Morphology and Its Applications to Image and Signal Processing (Computational Imaging and Vision),” Springer, 2000, ISBN: 0792378628.
  38. [38] J. Serra, “Image analysis and mathematical morphology,” Academic Press Inc., 1982.
  39. [39] G. Matheron, “Random Sets and Integral Geometry,” John Wiley and Sons, ISBN: 0-471-57621-2, 1975.
  40. [40] S.R. Sternberg, “Grayscale morphology,” Computer Vision Graphics and Image Processing, Vol.35, pp. 333-355, 1986.
  41. [41] Rafael C. Gonzalez and Richard E. Woods, “Digital image processing,” Prentice Hall, Second Edition, pp. 519-566, 2002.
  42. [42] J. Goutsias, L. Vincent, and Dan S. Bloomberg (Eds.), “Mathematical morphology and its applications to image and signal processing,” Kluwer Academic Publishers, Vol.18, 2000.
  43. [43] S. Rahnamayan, H.R. Tizhoosh, and M.M.A Salama, “Opposition-Based Differential Evolution,” IEEE Transactions on Evolutionary Computation, Vol.12, Issue 1, pp. 64-79, Feb. 2008.
  44. [44] S. Rahnamayan and H.R. Tizhoosh, “Image Thresholding Using Micro Opposition-Based Differential Evolution (Micro-ODE),” IEEE World Congress on Computational Intelligence (WCCI-2008), Hong Kong, pp. 1409-1416, June 2008.
  45. [45] S. Rahnamayan and G. Gary Wang, “Solving Large Scale Optimization Problems by Opposition-Based Differential Evolution (ODE),” World Scientific and Engineering Academy and Society, Transactions on Computers, Vol.7, Issue 10, pp. 1792-1804, Oct. 2008.

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