Abstract
It is not a surprise that image processing is a growing research field. Vision in general and images in particular have always played an important and essential role in human life. Not only as a way to communicate, but also for commercial, scientific, industrial and military applications. Many techniques have been introduced and developed to deal with all the challenges involved with image processing. In this paper, we will focus on techniques that find their origin in fuzzy set theory and fuzzy logic. We will show the possibilities of fuzzy logic in applications such as image retrieval, morphology and noise reduction by discussing some examples. Combined with other state-of-the-art techniques they deliver a useful contribution to current research.
Chapter PDF
References
Angulo, J.: Unified morphological color processing framework in a lum/sat/hue representation. In: Proceedings of ISMM 2005, International Symposium on Mathematical Morphology, France, pp. 387–396 (2005)
Brunelli, R., Mich, O.: Histograms analysis for image retrieval. Pattern Recognition 34, 1625–1637 (2001)
Chamorro-Martínez, J., Medina, J.M., Barranco, C., Galán-Perales, E., Soto-Hidalgo, J.M.: An approach to image retrieval on fuzzy object-relational database using dominant color descriptors. In: Proceedings of the 4th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT, pp. 676–684 (2005)
Chang, S.: Content-based indexing and retrieval of visual information. IEEE Signal Processing Magazine 14(4), 45–48 (1997)
Chen, S.M., Yeh, M.S., Hsiao, P.Y.: A comparison of similarity measures of fuzzy values. Fuzzy Sets and Systems 72, 79–89 (1995)
Comer, M.L., Delp, E.J.: Morphological operations for color image processing. Journal of Electronic Imaging 8(3), 279–289 (1999)
De Baets, B.: Fuzzy morphology: a logical approach. In: Ayyub, B.M., Gupta, M.M. (eds.) Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach, pp. 53–67. Kluwer Academic Publishers, Boston (1997)
Del Bimbo, A.: Visual Information Retrieval. Morgan Kaufmann Publishers, San Francisco (2001)
De Witte, V., Schulte, S., Nachtegael, M., Van der Weken, D., Kerre, E.E.: Vector morphological operators for colour images. In: Kamel, M., Campilho, A. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 667–675. Springer, Heidelberg (2005)
Hanbury, A., Serra, J.: Mathematical morphology in the CIELAB space. Image Analysis and Stereology 21(3), 201–206 (2002)
Haralick, R.M., Sternberg, S.R., Zhuang, X.: Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(4), 532–550 (1987)
Li, J., Li, Y.: Multivariate mathematical morphology based on principal component analysis: initial results in building extraction. International Archives for Photogrammetry, Remote Sensing and Spatial Information Sciences 35(B7), 1168–1173 (2004)
Louverdis, G., Andreadis, I., Tsalides, P.: New fuzzy model for morphological color image processing. In: Proceedings of IEEE Vision, Image and Signal Processing, pp. 129–139 (2002)
Lu, G., Phillips, J.: Using perceptually weighted histograms for colour-based image retrieval. In: Proceedings of the 4th International Conference on Signal Processing, pp. 1150–1153 (1998)
Nachtegael, M., Kerre, E.E.: Connections between binary, gray-scale and fuzzy mathematical morphologies. Fuzzy Sets and Systems 124(1), 73–86 (2001)
Nachtegael, M., Schulte, S., Van der Weken, D., De Witte, V., Kerre, E.E.: Fuzzy filters for noise reduction: the case of impulse noise. In: Proceedings of SCIS-ISIS (2004)
Nachtegael, M., Schulte, S., Van der Weken, D., De Witte, V., Kerre, E.E.: Fuzzy Filters for noise reduction: the case of gaussian noise. In: Proceedings of FUZZ-IEEE (2005)
Nachtegael, M., Schulte, S., De Witte, V., Mélange, T., Kerre, E.E.: Color image retrieval using fuzzy similarity measures and fuzzy partitions. In: Proceedings of ICIP 2007, 14th International Conference on Image Processing, 7th edn., San Antonio, USA (2007)
Omhover, J.F., Detyniecki, M., Rifqi, M., Bouchon-Meunier, B.: Ranking invariance between fuzzy similarity measures applied to image retrieval. In: Proceedings of the 2004 IEEE International Conference on Fuzzy Systems, pp. 1367–1372. IEEE, Los Alamitos (2004)
Schulte, S., Nachtegael, M., De Witte, V., Van der Weken, D., Kerre, E.E.: A new two step color filter for impulse noise. In: Proceedings of the 11th Zittau Fuzzy Colloquium, pp. 185–192 (2004)
Schulte, S., Nachtegael, M., De Witte, V., Van der Weken, D., Kerre, E.E.: A fuzzy impulse noise detection and reduction method. IEEE Transactions on Image Processing 15(5), 1153–1162 (2006)
Schulte, S., De Witte, V., Nachtegael, M., Van der Weken, D., Kerre, E.E.: A new fuzzy filter for the reduction of randomly valued impulse noise. In: Proceedings of ICIP 2006, 13th International Conference on Image Processing, Atlanta, USA, pp. 1809–1812 (2006)
Schulte, S., Nachtegael, M., De Witte, V., Van der Weken, D., Kerre, E.E.: Fuzzy impulse noise reduction methods for color images. In: Proceedings of FUZZY DAYS 2006, International Conference on Computational Intelligence, Dortmund (Germany), pp. 711–720 (2006)
Schulte, S., De Witte, V., Nachtegael, M., Mélange, T., Kerre, E.E.: A new fuzzy additive noise reduction method. In: Image Analysis and Recognition - Proceedings of ICIAR 2007. LNCS, vol. 4633, pp. 12–23. Springer, Heidelberg (2007)
Serra, J.: Image analysis and mathematical morphology. Academic Press Inc, Londen (1982)
Sharma, G.: Digital Color Imaging Handbook. CRC Press, Boca Raton, USA (2003)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1379 (2000)
Stanchev, P., Green, D., Dimitrov, B.: High level color similarity retrieval. International Journal of Information Theories & Applications 10(3), 283–287 (2003)
Stanchev, P.: Using image mining for image retrieval. In: Proceedings of the IASTED International Conference on Computer Science and Technology, pp. 214–218 (2003)
Van der Weken, D., Nachtegael, M., Kerre, E.E.: The applicability of similarity measures in image processing. Intellectual Systems 6(1-4), 231–248 (2001)
Van der Weken, D., Nachtegael, M., Kerre, E.E.: An overview of similarity measures for images. In: Proceedings of ICASSP 2002, IEEE International Conference on Acoustics, Speech and Signal Processing, Orlando, USA, pp. 3317–3320 (2002)
Van der Weken, D., Nachtegael, M., Kerre, E.E.: Using similarity measures for histogram comparison. In: De Baets, B., Kaynak, O., Bilgiç, T. (eds.) IFSA 2003. LNCS, vol. 2715, pp. 396–403. Springer, Heidelberg (2003)
Van der Weken, D., Nachtegael, M., Kerre, E.E.: Using similarity measures and homogeneity for the comparison of images. Image and Vision Computing 22(9), 695–702 (2004)
Van der Weken, D., De Witte, V., Nachtegael, M., Schulte, S., Kerre, E.E.: A component-based and vector-based approach for the construction of quality measures for colour images. In: Proceedings of IPMU 2006, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Paris (France), pp. 1548–1555 (2006)
Van der Weken, D., De Witte, V., Nachtegael, M., Schulte, S., Kerre, E.E.: Fuzzy similarity measures for colour images. In: Proceedings of CIS-RAM 2006, IEEE International Conferences on Cybernetics & Intelligent Systems and Robotics, Automation & Mechatronics, Bangkok (Thailand), pp. 806–810 (2006)
Vansteenkiste, E., Van der Weken, D., Philips, W., Kerre, E.E.: Psycho-visual evaluation of fuzzy similarity measures. In: Proceedings of SPS-DARTS 2006, 2nd Annual IEEE BENELUX/DSP Valley Signal Processing Symposium, Antwerp (Belgium), pp. 127–130 (2006)
Vansteenkiste, E., Van der Weken, D., Philips, W., Kerre, E.E.: Evaluation of the perceptual performance of fuzzy image quality measures. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4251, pp. 623–630. Springer, Heidelberg (2006)
Zadeh, L.: Fuzzy Sets. Information Control 8, 338–353 (1965)
Zlokolica, V., De Geyter, M., Schulte, S., Pizurica, A., Philips, W., Kerre, E.E.: Fuzzy logic recursive change detection for tracking and denoising of video sequences. In: Proceedings of IS&T/SPIE, 17th Annual Symposium Electronic Imaging Science and technology, Video Communications and Processing, vol. 5685, pp. 771–782 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nachtegael, M., Mélange, T., Kerre, E.E. (2007). The Possibilities of Fuzzy Logic in Image Processing. In: Ghosh, A., De, R.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2007. Lecture Notes in Computer Science, vol 4815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77046-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-77046-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77045-9
Online ISBN: 978-3-540-77046-6
eBook Packages: Computer ScienceComputer Science (R0)