Advertisement

Edge Detection Using Cellular Automata

  • Paul L. RosinEmail author
  • Xianfang Sun
Chapter
Part of the Emergence, Complexity and Computation book series (ECC, volume 10)

Abstract

Edge detection has been a long standing topic in image processing, generating hundreds of papers and algorithms over the last 50 years. Likewise, the topic has had a fascination for researchers in cellular automata, who have also developed a variety of solutions, particularly over the last ten years. CA based edge detection has potential benefits over traditional approaches since it is computationally efficient, and can be tuned for specific applications by appropriate selection or learning of rules. This chapter will provide an overview of CA based edge detection techniques, and assess their relative merits and weaknesses. Several CA based edge detection methods are implemented and tested to enable an initial comparison between competing approaches.

Keywords

Cellular Automaton Edge Detection Cellular Automaton Cellular Neural Network Edge Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graph. 26(3), 10 (2007)CrossRefGoogle Scholar
  2. 2.
    Baştürk, A., Günay, E.: Efficient edge detection in digital images using a cellular neural network optimized by differential evolution algorithm. Expert Syst. Appl. 36(2), 2645–2650 (2009)CrossRefGoogle Scholar
  3. 3.
    Batouche, M., Meshoul, S., Abbassene, A.: On solving edge detection by emergence. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, pp. 800–808. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence 8, 679–698 (1986)CrossRefGoogle Scholar
  5. 5.
    Chang, C., Zhang, Y., Gdong, Y.: Cellular automata for edge detection of images. Int. Conf. on Machine Learning and Cybernetics 6, 3830–3834 (2004)CrossRefGoogle Scholar
  6. 6.
    Chen, Y., Yan, Z.: A cellular automatic method for the edge detection of images. In: Huang, D.-S., Wunsch II, D.C., Levine, D.S., Jo, K.-H. (eds.) ICIC 2008. LNCS (LNAI), vol. 5227, pp. 935–942. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Diwakar, M., Patel, P., Gupta, K.: Cellular automata based edge-detection for brain tumor. In: Advances in Computing, Communications and Informatics, pp. 53–59 (2013)Google Scholar
  8. 8.
    Ens, J., Lawrence, P.: An investigation of methods for determining depth from focus. IEEE Trans. Pattern Analysis and Machine Intelligence 15(2), 97–108 (1993)CrossRefGoogle Scholar
  9. 9.
    Georgilas, I., Gale, E., Adamatzky, A., Melhuish, C.: UAV horizon tracking using memristors and cellular automata visual processing (2013)Google Scholar
  10. 10.
    Gharehchopogh, F., Ebrahimi, S.: A novel approach for edge detection in images based on cellular learning automata. Int. J. Computer Vision and Image Processing 2(4), 51–61 (2012)CrossRefGoogle Scholar
  11. 11.
    Gorsevski, P., Onasch, C., Farver, J., Ye, X.: Detecting grain boundaries in deformed rocks using a cellular automata approach. Computers & Geosciences 42, 136–142 (2012)CrossRefGoogle Scholar
  12. 12.
    Heath, M., Sarkar, S., Sanocki, T., Bowyer, K.: Robust visual method for assessing the relative performance of edge detection algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 19(12), 1338–1359 (1997)CrossRefGoogle Scholar
  13. 13.
    Heath, M.D., Sarkar, S., Sanocki, T.A., Bowyer, K.W.: Comparison of edge detectors: A methodology and initial study. Computer Vision and Image Understanding 69(1), 38–54 (1998)CrossRefGoogle Scholar
  14. 14.
    Kazar, O., Slatnia, S.: Evolutionary cellular automata for image segmentation and noise filtering using genetic algorithms. Journal of Applied Computer Science and Mathematics 5(10), 33–40 (2011)Google Scholar
  15. 15.
    Kumar, T., Sahoo, G.: A novel method of edge detection using cellular automata. International Journal of Computer Applications 9(4), 38–44 (2010)CrossRefGoogle Scholar
  16. 16.
    Lee, M., Bruce, L.: Applying cellular automata to hyperspectral edge detection. In: Int. Geoscience and Remote Sensing Symposium, pp. 2202–2205 (2010)Google Scholar
  17. 17.
    Li, H., Liao, X., Li, C., Huang, H., Li, C.: Edge detection of noisy images based on cellular neural networks. Communications in Nonlinear Science and Numerical Simulation 16(9), 3746–3759 (2011)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. Pattern Analysis and Machine Intelligence 26(5), 530–549 (2004)CrossRefGoogle Scholar
  19. 19.
    Men, H., Zhang, J., Wang, C.: Measurement of inhibition zone based on cellular automata edge detection method. In: Int. Workshop on Education Technology and Computer Science, vol. 2, pp. 357–360 (2009)Google Scholar
  20. 20.
    Mirzaei, K., Motameni, H., Enayatifar, R.: New method for edge detection and denoising via fuzzy cellular automata. Int. J. Phy. Sci. 6(13), 3175–3180 (2011)Google Scholar
  21. 21.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. SMC 9, 62–66 (1979)Google Scholar
  22. 22.
    Peer, M., Qadir, F., Khan, K.: Investigations of cellular automata game of life rules for noise filtering and edge detection. Int. J. Information Engineering and Electronic Business 4(2), 22–28 (2012)CrossRefGoogle Scholar
  23. 23.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence 12(7), 629–639 (1990)CrossRefGoogle Scholar
  24. 24.
    Piao, Y., Kim, S., Cho, S.J.: Two-dimensional cellular automata transforms for a novel edge detection. In: IComputability in Europe 2008, Logic and Theory of Algorithms (2008)Google Scholar
  25. 25.
    Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Image registration by maximization of combined mutual information and gradient information. IEEE Trans. Med. Imaging 19(8), 809–814 (2000)CrossRefGoogle Scholar
  26. 26.
    Popovici, A., Popovici, D.: Cellular automata in image processing. In: Int. Symp. on the Mathematical Theory of Networks and Systems (2002)Google Scholar
  27. 27.
    Priego, B., Bellas, F., Souto, D., López-Peña, F., Duro, R.: Evolving cellular automata for detecting edges in hyperspectral images. In: Int. Conf. on Fuzzy Systems, pp. 1–6 (2012)Google Scholar
  28. 28.
    Pudil, P., Novovicova, J., Kittler, J.: Floating search methods in feature-selection. Pattern Recognition Letters 15(11), 1119–1125 (1994)CrossRefGoogle Scholar
  29. 29.
    Qadir, F., Khan, K.: Investigations of cellular automata linear rules for edge detection. Int. J. Computer Network and Information Security 3, 47–53 (2013)Google Scholar
  30. 30.
    Qadir, F., Peer, M., Khan, K.: Efficient edge detection methods for diagnosis of lung cancer based on two-dimensional cellular automata. Advances in Applied Science Research 3(4), 2050–2058 (2012)Google Scholar
  31. 31.
    Roberts, L.: Machine Perception of Three-Dimensional Solids. In: Outstanding Dissertations in the Computer Sciences. Garland Publishing, New York (1963)Google Scholar
  32. 32.
    Rosin, P.: Training cellular automata for image processing. IEEE Trans. on Image Processing 15(7), 2076–2087 (2006)CrossRefGoogle Scholar
  33. 33.
    Rosin, P.: A simple method for detecting salient regions. Pattern Recognition 42(11), 2363–2371 (2009)CrossRefzbMATHGoogle Scholar
  34. 34.
    Rosin, P.: Image processing using 3-state cellular automata. Computer Vision and Image Understanding 114(7), 790–802 (2010)CrossRefGoogle Scholar
  35. 35.
    Sahota, P., Daemi, M., Elliman, D.: Training genetically evolving cellular automata for image processing. In: Int. Symp. Speech, Image Processing and Neural Networks, pp. 753–756 (1994)Google Scholar
  36. 36.
    Sato, S., Kanoh, H.: Evolutionary design of edge detector using rule-changing cellular automata. In: Nature & Biologically Inspired Computing, pp. 60–65 (2010)Google Scholar
  37. 37.
    Selvapeter, J., Hordijk, W.: Genetically evolved cellular automata for image edge detection. In: Proceedings of the International Conference on Signal, Image Processing and Pattern Recognition, SIPP 2013 (2013)Google Scholar
  38. 38.
    Selvapeter, P.J., Hordijk, W.: Cellular automata for image noise filtering. In: Nature & Biologically Inspired Computing, pp. 193–197 (2009)Google Scholar
  39. 39.
    Senthilkumar, S., Piah, A.R.M.: An improved fuzzy cellular neural network (IFCNN) for an edge detection based on parallel RK(5,6) approach. International Journal of Computational Systems Engineering 1(1), 70–78 (2012)CrossRefGoogle Scholar
  40. 40.
    Shin, M.C., Goldgof, D.B., Bowyer, K.W.: Comparison of edge detector performance through use in an object recognition task. Computer Vision and Image Understanding 84(1), 160–178 (2001)CrossRefzbMATHGoogle Scholar
  41. 41.
    Slatnia, S., Batouche, M., Melkemi, K.E.: Evolutionary cellular automata based-approach for edge detection. In: Masulli, F., Mitra, S., Pasi, G. (eds.) WILF 2007. LNCS (LNAI), vol. 4578, pp. 404–411. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  42. 42.
    Suyi, L., Qian, W., Heng, Z.: Edge detection of fabric defect based on fuzzy cellular automata. In: Int. Workshop on Intelligent Systems and Applications, pp. 1–3 (2009)Google Scholar
  43. 43.
    Wongthanavasu, S.: Cellular automata for medical image processing. In: Salcido, A. (ed.) Cellular Automata – Innovative Modelling for Science and Engineering, pp. 395–410 (2011)Google Scholar
  44. 44.
    Wongthanavasu, S., Lursinsap, C.: A 3-D CA-based edge operator for 3-D images. In: Int. Conf. Image Processing, pp. 235–238 (2004)Google Scholar
  45. 45.
    Wongthanavasu, S., Sadananda, R.: A CA-based edge operator and its performance evaluation. J. Visual Communication and Image Representation 14(2), 83–96 (2003)CrossRefGoogle Scholar
  46. 46.
    Yang, C., Ye, H., Wang, G.: Cellular automata modeling in edge recognition. In: 7th Int. Symp. on Artificial Life and Robotics, pp. 128–132 (2002)Google Scholar
  47. 47.
    Zhang, K., Zhang, W., Yuan, J.: Edge detection of images based on cloud model cellular automata. In: Chinese Control Conference, pp. 249–253 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Computer Science & InformaticsCardiff UniversityCardiffUK

Personalised recommendations