Computational Intelligence in Multimedia Processing: Foundation and Trends

  • Aboul-Ella Hassanien
  • Ajith Abraham
  • Janusz Kacprzyk
  • James F. Peters
Part of the Studies in Computational Intelligence book series (SCI, volume 96)

This chapter presents a broad overview of Computational Intelligence (CI) techniques including Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough Sets (RS). In addition, a very brief introduction to near sets and near images which offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving multimedia problems is presented. A review of the current literature on CI based approaches to various problems in multimedia computing such as speech, audio and image processing, video watermarking, content-based multimedia indexing and retrieval are presented. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to resolve multimedia computing problems and how multimedia could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are also presented.


Automatic Speech Recognition Watermark Scheme Speech Recognition System Adaptive Resonance Theory Audio Watermark 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abraham A., Jain R., Thomas J., and Han S.Y. (2007) D-SCIDS: Distributed soft computing intrusion detection systems. Journal of Network and Computer Applications, vol. 30, no. 1, pp. 81–98.CrossRefGoogle Scholar
  2. 2.
    Bishop C.M. (1995) Neural Networks for Pattern Recognition. Oxford University Press, Oxford.Google Scholar
  3. 3.
    Kohonen T. (1988) Self-Organization and Associative Memory. Springer, Berlin Heidelberg New York.zbMATHGoogle Scholar
  4. 4.
    Carpenter G. and Grossberg S. (1995) Adaptive Resonance Theory (ART). In: Arbib M.A. (ed.), The Handbook of Brain Theory and Neural Networks. MIT, Cambridge, pp. 79–82.Google Scholar
  5. 5.
    Grossberg S. (1976) Adaptive pattern classification and universal recoding: Parallel development and coding of neural feature detectors. Biological Cybernetics, vol. 23, pp. 121–134.zbMATHCrossRefGoogle Scholar
  6. 6.
    Abraham A. (2001) Neuro-fuzzy systems: State-of-the-art modeling techniques, connectionist models of neurons, learning processes, and artificial intelligence. In: Jose Mira and Alberto Prieto (eds.), Lecture Notes in Computer Science, vol. 2084, Springer, Berlin Heidelberg New York, pp. 269–276.Google Scholar
  7. 7.
    Nguyen H.T. and Walker E.A. (1999) A First Course in Fuzzy Logic. CRC, Boca Raton.Google Scholar
  8. 8.
    Hassanien A.E. and Jafar Ali (2003) Image classification and retrieval algorithm based on rough set theory. South African Computer Journal (SACJ), vol. 30, pp. 9–16.Google Scholar
  9. 9.
    Hassanien A.E. (2006) Hiding iris data for authentication of digital images using wavelet theory. International journal of Pattern Recognition and Image Analysis, vol. 16, no. 4, pp. 637–643.CrossRefGoogle Scholar
  10. 10.
    Hassanien A.E., Ali J.M., and Hajime N. (2004) Detection of spiculated masses in Mammograms based on fuzzy image processing. In: 7th International Conference on Artificial Intelligence and Soft Computing, ICAISC2004, Zakopane, Poland, 7–11 June. Lecture Notes in Artificial Intelligence, vol. 3070. Springer, Berlin Heidelberg New York, pp. 1002–1007.Google Scholar
  11. 11.
    Fogel L.J., Owens A.J., and Walsh M.J. (1967) Artificial Intelligence Through Simulated Evolution. Wiley, New York.Google Scholar
  12. 12.
    Fogel D.B. (1999) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 2nd edition. IEEE, Piscataway, NJ.Google Scholar
  13. 13.
    Pearl J. (1997) Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco.Google Scholar
  14. 14.
    Dubois D., Prade H., and Sdes F. (2001) Fuzzy logic techniques in multimedia database querying: A preliminary investigation of the potentials. IEEE Transactions on Knowledge and Data Engineering, vol. 13 , no. 3, pp. 383–392.CrossRefGoogle Scholar
  15. 15.
    Holland J. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Harbor.Google Scholar
  16. 16.
    Goldberg D.E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading.Google Scholar
  17. 17.
    Zlokolica V., Piurica A., Philips W., Schulte S., and Kerre E. (2006) Fuzzy logic recursive motion detection and denoising of video sequences. Journal of Electronic Imaging, vol. 15, no. 2.Google Scholar
  18. 18.
    Koza J.R. (1992) Genetic Programming. MIT, Cambridge, MA.zbMATHGoogle Scholar
  19. 19.
    Buscicchio C.A., Grecki P., and Caponetti L. (2006) Speech emotion recognition using spiking neural networks. In: Esposito F., Ras Z.W., Malerba D., and Semeraro G. (eds.), Foundations of Intelligent Systems, Lecture Notes in Computer Science, vol. 4203, Springer, Berlin Heidelberg New York, pp. 38–46.Google Scholar
  20. 20.
    Ford R.M. (2005) Fuzzy logic methods for video shot boundary detection and classification. In: Tan Y.-P., Yap K.H., and Wang L. (eds.) Intelligent Multimedia Processing with Soft Computing, Studies in Fuzziness and Soft Computing, vol. 168, Springer, Berlin Heidelberg New York, pp. 151–169.CrossRefGoogle Scholar
  21. 21.
    Back T. (1996) Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, New York.Google Scholar
  22. 22.
    Ming Li and Tong Wang (2005) An approach to image retrieval based on concept lattices and rough set theory. Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, 5–8 Dec., pp. 845–849.Google Scholar
  23. 23.
    Kulkarni S. (2004) Neural-fuzzy approach for content-based retrieval of digital video. Canadian Conference on Electrical and Computer Engineering, vol. 4, 2–5 May, pp. 2235–2238.Google Scholar
  24. 24.
    Hui Fang, Jianmin Jiang, and Yue Feng (2006) A fuzzy logic approach for detection of video shot boundaries. Pattern Recognition, vol. 39, no. 11, pp. 2092–2100.zbMATHCrossRefGoogle Scholar
  25. 25.
    Selouani S.-A. and O’Shaughnessy D. (2003) On the use of evolutionary algorithms to improve the robustness of continuous speech recognition systems in adverse conditions. EURASIP Journal on Applied Signal Processing, vol. 8, pp. 814–823Google Scholar
  26. 26.
    Lo C.-C. and Wang S.-J. (2001) Video segmentation using a histogram-based fuzzy C-means clustering algorithm. The 10th IEEE International Conference on Fuzzy Systems, vol. 2, 2–5 Dec., pp. 920–923.Google Scholar
  27. 27.
    Cao X. and Suganthan P.N. (2002) Neural network based temporal video segmentation. International Journal of Neural Systems, vol. 12, no. 3–4, pp. 263–629.CrossRefGoogle Scholar
  28. 28.
    Chang C.-H., Ye Z., and Zhang M. (2005) Fuzzy-ART based adaptive digital watermarking scheme. IEEE Transactions on Circuits and Systems for Video Technology, vol. 15, no. 1, pp. 65–81.CrossRefGoogle Scholar
  29. 29.
    Diego Sal Diaz and Manuel Grana Romay (2005) Introducing a watermarking with a multi-objective genetic algorithm. Proceedings of the 2005 conference on Genetic and evolutionary computation, Washington DC, USA, pp. 2219–2220.Google Scholar
  30. 30.
    Lou D.-C. and Yin T.-L. (2001) Digital watermarking using fuzzy clustering technique. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (Japan), vol. E84-A, no. 8, pp. 2052–2060.Google Scholar
  31. 31.
    Maher El-arbi, Ben Amar, and C. Nicolas, H. (2006) Video watermarking based on neural networks. IEEE International Conference on Multimedia and Expo, Toronto, Canada, pp. 1577–1580.Google Scholar
  32. 32.
    Der-Chyuan Lou, Jieh-Ming Shieh, and Hao-Kuan Tso (2005) Copyright protection scheme based on chaos and secret sharing techniques. Optical Engineering, vol. 44, no. 11, pp. 117004–117010.CrossRefGoogle Scholar
  33. 33.
    Wei Lu, Hongtao Lu, and FuLai Chung (2005) Subsampling-based robust watermarking using neural network detector. Advances in Neural Networks, ISNN 2005, Lecture Notes in Computer Science, vol. 3497, pp. 801–806.Google Scholar
  34. 34.
    Cheng-Ri Piao, Sehyeong Cho, and Seung-Soo Han (2006) Color image watermarking algorithm using BPN neural networks. Neural Information Processing, Lecture Notes in Computer Science, vol. 4234, pp. 234–242CrossRefGoogle Scholar
  35. 35.
    Zheng Liu, Xue Li, and Dong Z. (2004) Multimedia authentication with sensor-based watermarking. Proc. of the international workshop on Multimedia and security, Magdeburg, Germany, pp. 155–159Google Scholar
  36. 36.
    Hung-Hsu Tsai, Ji-Shiung Cheng, and Pao-Ta Yu (2003) Audio watermarking based on HAS and neural networks in DCT domain. EURASIP Journal on Applied Signal Processing, vol. 2003, no. 3, pp. 252–263CrossRefGoogle Scholar
  37. 37.
    Cao L., Wang X., Wang Z., and Bai S. (2005) Neural network based audio watermarking algorithm. In: ICMIT 2005: Information Systems and Signal Processing, Wei Y., Chong K.T., Takahashi T. (eds.), Proceedings of the SPIE, vol. 6041, pp. 175–179Google Scholar
  38. 38.
    Alessandro Bugatti, Alessandra Flammini, and Pierangelo Migliorati (2002) Audio classification in speech and music: A comparison between a statistical and a neural approach. EURASIP Journal on Applied Signal Processing, vol. 2002, no. 4, pp. 372–378.zbMATHCrossRefGoogle Scholar
  39. 39.
    Lim Ee Hui, Seng K.P., and Tse K.M. (2004) RBF Neural network mouth tracking for audio–visual speech recognition system. IEEE Region 10 Conference TENCON2004, 21–24 Nov., pp. 84–87.Google Scholar
  40. 40.
    Jian Zhou, Guoyin Wang, Yong Yang, and Peijun Chen (2006) Speech emotion recognition based on rough set and SVM. 5th IEEE International Conference on Cognitive Informatics ICCI 2006, 17–19 July, vol. 1, pp. 53–61.CrossRefGoogle Scholar
  41. 41.
    Faraj M.-I. and Bigun J. (2007) Audio–visual person authentication using lip-motion from orientation maps. Pattern Recognition Letters, vol. 28, no. 11, pp. 1368–1382.CrossRefGoogle Scholar
  42. 42.
    Halavati R., Shouraki S.B., Eshraghi M., Alemzadeh M., and Ziaie P. (2004) A novel fuzzy approach to speech recognition. Hybrid Intelligent Systems. Fourth International Conference on Hybrid Intelligent Systems, 5–8 Dec., pp. 340–345.Google Scholar
  43. 43.
    Eugene I. Bovbel and Dzmitry V. Tsishkou (2000) Belarussian speech recognition using genetic algorithms. Third International Workshop on Text, Speech and Dialogue, Brno, Czech Republic, pp. 185–204.Google Scholar
  44. 44.
    Fellenz W.A., Taylor J.G., Cowie R., Douglas-Cowie E., Piat F., Kollias S., Orovas C., and Apolloni B. (2000) On emotion recognition of faces and of speech using neural networks, fuzzy logic and the ASSESS system. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 2, IJCNN 2000, pp. 93–98.Google Scholar
  45. 45.
    Laaksonen J., Koskela M., and Oja E. (2002) PicSOM-Self-organizing image retrieval with MPEG-7 content descriptions. IEEE Transactions on Neural Networks, Special Issue on Intelligent Multimedia Processing vol. 13, no. 4, pp. 841–853.Google Scholar
  46. 46.
    Mats S., Jorma L., Matti P., and Timo H. (2006) Retrieval of multimedia objects by combining semantic information from visual and textual descriptors. Proceedings of 16th International Conference on Artificial Neural Networks (ICANN 2006), pp. 75–83, Athens, Greece, September 2006.Google Scholar
  47. 47.
    Kostek B. and Andrzej C. (2001) Employing fuzzy logic and noisy speech for automatic fitting of hearing aid. 142 Meeting of the Acoustical Society of America, No. 5, vol. 110, pp. 2680, Fort Lauderdale, USA.Google Scholar
  48. 48.
    Liu J., Wang Z., and Xiao X. (2007) A hybrid SVM/DDBHMM decision fusion modeling for robust continuous digital speech recognition. Pattern Recognition Letter, vol. 28, No. 8, pp. 912–920.CrossRefGoogle Scholar
  49. 49.
    Ing-Jr Ding (2007) Incremental MLLR speaker adaptation by fuzzy logic control. Pattern Recognition, vol. 40 , no. 11, pp. 3110–3119zbMATHCrossRefGoogle Scholar
  50. 50.
    Andrzej C. (2003) Automatic identification of sound source position employing neural networks and rough sets. Pattern Recognition Letters, vol. 24, pp. 921–933.CrossRefGoogle Scholar
  51. 51.
    Andrzej C., Kostek B., and Henryk S. (2002) Diagnostic system for speech articulation and speech understanding. 144th Meeting of the Acoustical Society of America (First Pan-American/Iberian Meeting on Acoustics), Journal of the Acoustical Society of America, vol. 112, no. 5, Cancun, Mexico.Google Scholar
  52. 52.
    Andrzej C., Andrzej K., and Kostek B. (2003) Intelligent processing of stuttered speech. Journal of Intelligent Information Systems, vol. 21, no. 2, pp. 143–171.CrossRefGoogle Scholar
  53. 53.
    Pawel Zwan, Piotr Szczuko, Bozena Kostek, and Andrzej Czyzewski (2007) Automatic singing voice recognition employing neural networks and rough sets. RSEISP 2007, pp. 793–802.Google Scholar
  54. 54.
    Andrzej C. and Marek S. (2002) Pitch estimation enhancement employing neural network-based music prediction. Proc. IASTED Intern. Conference, Artificial Intelligence and Soft Computing, pp. 413–418, Banff, Canada.Google Scholar
  55. 55.
    Hendessi F., Ghayoori A., and Gulliver T.A. (2005) A speech synthesizer for Persian text using a neural network with a smooth ergodic HMM. ACM Transactions on Asian Language Information Processing (TALIP), vol. 4, no. 1, pp. 38–52.CrossRefGoogle Scholar
  56. 56.
    Orhan Karaali, Gerald Corrigan, and Ira Gerson (1996) Speech synthesis with neural networks. World Congress on Neural Networks, San Diego, Sept. 1996, pp. 45–50.Google Scholar
  57. 57.
    Corrigan G., Massey N., and Schnurr O. (2000) Transition-based speech synthesis using neural networks. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, pp. 945–948.Google Scholar
  58. 58.
    Shan Meng and Youwei Zhang (2003) A method of visual speech feature area localization. Proceedings of the International Conference on Neural Networks and Signal Processing, 2003, vol. 2, 14–17 Dec., pp. 1173–1176.CrossRefGoogle Scholar
  59. 59.
    Sun-Yuan Kung and Jenq-Neng Hwang (1998) Neural networks for intelligent multimedia processing. Proceedings of the IEEE Workshop on Neural Networksm, vol. 86, no. 6, pp. 1244–1272.Google Scholar
  60. 60.
    Frankel J., Richmond K., King S., and Taylor P. (2000) An automatic speech recognition system using neural networks and linear dynamic models to recover and model articulatory traces. Proc. ICSLP, 2000.Google Scholar
  61. 61.
    Schuller B., Reiter S., and Rigoll G. (2006) Evolutionary feature generation in speech emotion. IEEE International Conference on Recognition Multimedia, pp. 5–8.Google Scholar
  62. 62.
    Lewis T.W. and Powers D.M.W., Audio–visual speech recognition using red exclusion and neural networks. Proceedings of the twenty-fifth Australasian conference on Computer science, vol. 4, Melbourne, Victoria, Australia, pp. 149–156.Google Scholar
  63. 63.
    Nakamura S. (2002) Statistical multimodal integration for audio–visual speech processing. IEEE Transactions on Neural Networks, vol. 13, no. 4, pp. 854–866.CrossRefGoogle Scholar
  64. 64.
    Guido R.C., Pereira J.C., and Slaets J.F.W. (2007) Advances on pattern recognition for speech and audio processing. Pattern Recognition Letters, vol. 28, no. 11, pp. 1283–1284.CrossRefGoogle Scholar
  65. 65.
    Vahideh Sadat Sadeghi and Khashayar Yaghmaie (2006) Vowel recognition using neural networks. International Journal of Computer Science and Network Security (IJCSNS), vol. 6, no. 12, pp. 154–158.Google Scholar
  66. 66.
    Hartigan J.A. and Wong M.A. (1979) Algorithm AS136: A K-means clustering algorithm. Applied Statistics, vol. 28, pp. 100–108.zbMATHCrossRefGoogle Scholar
  67. 67.
    Henry C. and Peters J.F. (2007) Image pattern recognition using approximation spaces and near sets. In: Proceedings of Eleventh International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007), Joint Rough Set Symposium (JRS 2007), Lecture Notes in Artificial Intelligence, vol. 4482, pp. 475–482.Google Scholar
  68. 68.
    Kerre E. and Nachtegael M. (2000) Fuzzy techniques in image processing: Techniques and applications. Studies in Fuzziness and Soft Computing, vol. 52, Physica, Heidelberg.Google Scholar
  69. 69.
    Lingras P. and West C. (2004) Interval set clustering of web users with rough K-means. Journal of Intelligent Information Systems, vol. 23, no. 1, pp. 5–16.zbMATHCrossRefGoogle Scholar
  70. 70.
    Lingras P. (2007) Applications of rough set based K-means, Kohonen, GA Clustering. Transactions on Rough Sets, VII, pp. 120–139.Google Scholar
  71. 71.
    Mitra Sushmita (2004) An evolutionary rough partitive clustering. Pattern Recognition Letters, vol. 25, pp. 1439–1449.CrossRefGoogle Scholar
  72. 72.
    Ng H.P., Ong S.H., Foong K.W.C., Goh P.S., and Nowinski, W.L. (2006) Medical image segmentation using K-means clustering and improved watershed algorithm. IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 61–65.Google Scholar
  73. 73.
    Nachtegael M., Van-Der-Weken M., Van-De-Ville D., Kerre D., Philips W., and Lemahieu I. (2001) An overview of classical and fuzzy-classical filters for noise reduction. 10th International IEEE Conference on Fuzzy Systems FUZZ-IEEE 2001, Melbourne, Australia, pp. 3–6.Google Scholar
  74. 74.
    Ning S., Ziarko W., Hamilton J., and Cercone N. (1995) Using rough sets as tools for knowledge discovery. In: Fayyad U.M. and Uthurusamy R. (eds.), First International Conference on Knowledge Discovery and Data Mining KDD’95, Montreal, Canada, AAAI, pp. 263–268.Google Scholar
  75. 75.
    Pawlak Z. (1991) Rough sets – Theoretical aspects of reasoning about data. Kluwer, Dordrecht.zbMATHGoogle Scholar
  76. 76.
    Pawlak Z., Grzymala-Busse J., Slowinski R., and Ziarko W. (1995) Rough sets. Communications of the ACM, vol. 38, no. 11, pp. 88–95.CrossRefGoogle Scholar
  77. 77.
    Polkowski L. (2003) Rough Sets: Mathematical Foundations. Physica, Heidelberg.Google Scholar
  78. 78.
    Peters J.F. (2007) Near sets: Special theory about nearness of objects. Fundamenta Informaticae, vol. 75, no. 1–4, pp. 407–433.zbMATHGoogle Scholar
  79. 79.
    Peters J.F. (2007) Near sets. General theory about nearness of objects. Applied Mathematical Sciences, vol. 1, no. 53, pp. 2609–2029.zbMATHGoogle Scholar
  80. 80.
    Peters J.F., Skowron A., and Stepaniuk J. (2007) Nearness of objects: Extension of approximation space model. Fundamenta Informaticae, vol. 79, pp. 1–16.Google Scholar
  81. 81.
    Peters J.F. (2007) Near sets. Toward approximation space-based object recognition, In: Yao Y., Lingras P., Wu W.-Z, Szczuka M., Cercone N., Śle¸zak D. (eds.), Proc. of the Second Int. Conf. on Rough Sets and Knowledge Technology (RSKT07), Joint Rough Set Symposium (JRS07), Lecture Notes in Artificial Intelligence, vol. 4481, Springer, Berlin Heidelberg New York, pp. 22–33.Google Scholar
  82. 82.
    Peters J.F. and Ramanna S. (2007) Feature selection: Near set approach. In: Ras Z.W., Tsumoto S., and Zighed D.A. (eds.) 3rd Int. Workshop on Mining Complex Data (MCD’07), ECML/PKDD-2007, Lecture Notes in Artificial Intelligence, Springer, Berlin Heidelberg New York, in press.Google Scholar
  83. 83.
    Peters J.F., Skowron A., and Stepaniuk J. (2006) Nearness in approximation spaces. In: Lindemann G., Schlilngloff H. et al. (eds.), Proc. Concurrency, Specification & Programming (CS&P’2006), Informatik-Berichte Nr. 206, Humboldt-Universität zu Berlin, pp. 434–445.Google Scholar
  84. 84.
    Orłowska E. (1982) Semantics of vague concepts. Applications of rough sets. Institute for Computer Science, Polish Academy of Sciences, Report 469, 1982. See, also, Orłowska E., Semantics of vague concepts, In: Dorn G. and Weingartner P. (eds.), Foundations of Logic and Linguistics. Problems and Solutions, Plenum, London, 1985, pp. 465–482.Google Scholar
  85. 85.
    Orłowska E. (1990) Verisimilitude based on concept analysis. Studia Logica, vol. 49, no. 3, pp. 307–320.zbMATHCrossRefGoogle Scholar
  86. 86.
    Pawlak Z. (1981) Classification of objects by means of attributes. Institute for Computer Science, Polish Academy of Sciences, Report 429, 1981.Google Scholar
  87. 87.
    Pawlak Z. (1982) Rough sets. International Journal of Computing and Information Sciences, vol. 11, pp. 341–356.zbMATHCrossRefGoogle Scholar
  88. 88.
    Pawlak Z. and Skowron A. (2007) Rudiments of rough sets. Information Sciences, vol. 177, pp. 3–27.zbMATHCrossRefGoogle Scholar
  89. 89.
    Peters J.F. (2008) Classification of perceptual objects by means of features. International Journal of Information Technology and Intelligent Computing, vol. 3, no. 2, pp. 1–35.Google Scholar
  90. 90.
    Lockery D. and Peters J.F. (2007) Robotic target tracking with approximation space-based feedback during reinforcement learning. In: Proceedings of Eleventh International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2007), Joint Rough Set Symposium (JRS 2007), Lecture Notes in Artificial Intelligence, vol. 4482, pp. 483–490.Google Scholar
  91. 91.
    Peters J.F., Borkowski M., Henry C., and Lockery D. (2006) Monocular vision system that learns with approximation spaces. In: Ella A., Lingras P., Slezak D., and Suraj Z. (eds.), Rough Set Computing: Toward Perception Based Computing, Idea Group Publishing, Hershey, PA, pp. 1–22.Google Scholar
  92. 92.
    Peters J.F., Borkowski M., Henry C., Lockery D., Gunderson D., and Ramanna S. (2006) Line-crawling bots that inspect electric power transmission line equipment. Proc. 3rd Int. Conf. on Autonomous Robots and Agents 2006 (ICARA 2006), Palmerston North, NZ, 2006, pp. 39–44.Google Scholar
  93. 93.
    Peters J.F. (2008) Approximation and perception in ethology-based reinforcement learning. In: Pedrycz W., Skowron A., and Kreinovich V. (eds.), Handbook on Granular Computing, Wiley, New York, Ch. 30, pp. 1–41.Google Scholar
  94. 94.
    Peters J.F. and Borkowski M. (2004) K-means indiscernibility relation over pixels. Proc. 4th Int. Conf. on Rough Sets and Current Trends in Computing (RSCTC 2004), Uppsala, Sweden, 1–5 June, pp. 580–585.Google Scholar
  95. 95.
    Peters J.F. and Pedrycz W. (2007) Computational intelligence. In: EEE Encyclopedia. Wiley, New York, in press.Google Scholar
  96. 96.
    Peters J.F., Liting H., and Ramanna S. (2001) Rough neural computing in signal analysis. Computational Intelligence, vol. 17, no. 3, pp. 493–513.CrossRefGoogle Scholar
  97. 97.
    Peters J.F., Skowron A., Suraj Z., Rzasa W., Borkowski M. (2002) Clustering: A rough set approach to constructing information granules. Soft Computing and Distributed Processing. Proceedings of 6th International Conference, SCDP 2002, pp. 57–61.Google Scholar
  98. 98.
    Petrosino A. and Salvi G. (2006) Rough fuzzy set based scale space transforms and their use in image analysis. International Journal of Approximate Reasoning, vol. 41, no. 2, pp. 212–228.CrossRefGoogle Scholar
  99. 99.
    Shankar B.U. (2007) Novel classification and segmentation techniques with application to remotely sensed images. Transactions on Rough Sets, vol. VII, LNCS 4400, pp. 295–380.Google Scholar
  100. 100.
    Otto C.W. (2007) Motivating rehabilitation exercise using instrumented objects to play video games via a configurable universal translation peripheral, M.Sc. Thesis, Supervisors: Peters J.F. and Szturm T., Department of Electrical and Computer Engineering, University of Manitoba, 2007.Google Scholar
  101. 101.
    Szturm T., Peters J.F., Otto C., Kapadia N., and Desai A. (2008) Task-specific rehabilitation of finger-hand function using interactive computer gaming, Archives for Physical Medicine and Rehabilitation, submitted.Google Scholar
  102. 102.
    Sandeep Chandana and Rene V. Mayorga (2006) RANFIS: Rough adaptive neuro-fuzzy inference system. International Journal of Computational Intelligence, vol. 3, no. 4, pp. 289–295.Google Scholar
  103. 103.
    Swagatam Das, Ajith Abraham, and Subir Kumar Sarkar (2006) A hybrid rough set – Particle swarm algorithm for image pixel classification. Proceedings of the Sixth International Conference on Hybrid Intelligent Systems, 13–15 Dec., pp. 26–32.Google Scholar
  104. 104.
    Bezdek J.C., Ehrlich R., and Full W. (1984) FCM: The fuzzy C-means clustering algorithm. Computers and Geosciences, vol. 10, pp. 191–203.CrossRefGoogle Scholar
  105. 105.
    Cetin O., Kantor A., King S., Bartels C., Magimai-Doss M., Frankel J., and Livescu K. (2007) An articulatory feature-based tandem approach and factored observation modeling. IEEE International Conference on Acoustics, Speech and Signal, ICASSP2007, Honolulu, HI, vol. 4, pp. IV-645–IV-648.Google Scholar
  106. 106.
    Raducanu B., Grana M., and Sussner P. (2001) Morphological neural networks for vision based self-localization. IEEE International Conference on Robotics and Automation, ICRA2001, vol. 2, pp. 2059–2064.Google Scholar
  107. 107.
    Ahmed M.N., Yamany S.M., Nevin M., and Farag A.A. (2003) A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging, vol. 21, no. 3, pp. 193–199.CrossRefGoogle Scholar
  108. 108.
    Yan M.X.H. and Karp J.S. (1994) Segmentation of 3D brain MR using an adaptive K-means clustering algorithm. IEEE Conference on Nuclear Science Symposium and Medical Imaging, vol. 4, pp. 1529–1533.Google Scholar
  109. 109.
    Voges K.E., Pope N.K.L.I., and Brown M.R. (2002) Cluster analysis of marketing data: A comparison of K-means, rough set, and rough genetic approaches. In: Abbas H.A., Sarker R.A., and Newton C.S. (eds.), Heuristics and Optimization for Knowledge Discovery, Idea Group Publishing, pp. 208–216.Google Scholar
  110. 110.
    Chen C.W., Luo J.B., and Parker K.J. (1998) Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications. IEEE Transactions on Image Processing, vol. 7, no. 12, pp. 1673–1683.CrossRefGoogle Scholar
  111. 111.
    Davis K.J. and Najarian K. (2001) Maximizing strength of digital watermarks using neural networks. International Joint Conference on Neural Networks, IJCNN 2001, vol. 4, pp. 2893–2898.Google Scholar
  112. 112.
    Sankar K. Pal (2001) Fuzzy image processing and recognition: Uncertainties handling and applications. International Journal of Image and Graphics, vol. 1, no. 2, pp. 169–195.CrossRefGoogle Scholar
  113. 113.
    Yixin Chen and James Z. Wang (2002) A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1252–1267.CrossRefGoogle Scholar
  114. 114.
    Yu Wanga Mingyue Dingb, Chengping Zhoub, and Ying Hub (2006) Interactive relevance feedback mechanism for image retrieval using rough set. Knowledge-Based Systems, vol. 19, no. 8, pp. 696–703.CrossRefGoogle Scholar
  115. 115.
    Zadeh L.A. (1965) Fuzzy sets. Information and Control, vol. 8, pp. 338–353.zbMATHCrossRefGoogle Scholar
  116. 116.
    Zbigniew W. (1987) Rough approximation of shapes in pattern recognition. Computer Vision, Graphics, and Image Processing, vol. 40, no. 2, pp. 228–249.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Aboul-Ella Hassanien
    • 1
    • 2
  • Ajith Abraham
    • Janusz Kacprzyk
      • James F. Peters
        • 3
      1. 1.Information Technology Department, FCICairo UniversityOrmanEgypt
      2. 2.Information System Department, CBAKuwait UniversityKuwait
      3. 3.Department of Electrical and Computer EngineeringUniversity of ManitobaWinnipegCanada

      Personalised recommendations