Abstract
Data fusion is a long term of research in image processing that is becoming more and more relevant owing to the complementary developments of computer and sensory technologies. Although operator research related to soft-computing, specially in the field of fuzzy systems, has evolved considerably during this last two decades, implemented frameworks of data fusion for image processing take seldom into consideration this kind of operators. Most of pattern recognition systems with image fusion are still based in basic operators, e.g. minimum or product. The purpose of the here presented tutorial is to analyze this fact, present some of the fuzzy aggregation operators in the context of data fusion for image processing and show some applications where the usage of the fuzzy integral, one of these operators, increased the performance of image processing systems considering data fusion.
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References
M.A. Abidi, R.C. Gonzalez, eds. (1992). Data Fusion in Robotics and Machine Intelligence. San Diego: Academic Press.
F. Alkoot and J. Kittler (2000). Improving the performance of the Product Fusion Strategy. Proc. 15th International Conference on Pattern Recognition, 1CPR’2000, Barcelona, Catalonia.
N. Ayache, O. Faugueras (1989). Maintaining representations of the environment of a mobile robot. IEEE Trans. Robotics and Automatation, Vol. 5, No. 6:804–819.
W.G.K. Backhaus, R. Kliegl and J.S. Werner eds. (1998). Color Vision: Perspectives from Different Disciplines. Berlin: Walter de Gruyter.
G. Beliakov (2000) Aggregation operators as similarity relations. In Information, Uncertainty and Fusion, B. Bouchon-Menier et al. eds. Boston: Kluwer Academic Publishers.
S. Beucher (1982). Watersheds of functions and picture segmentation. IEEE Int. Conf on Acoustics, Speech and Signal Processing, Paris, 1928–1931.
L. Bogoni (2000). Extending Dynamic Range of Monochrome and Color Images through Fusion. Proc. Int. Conf Pattern Recognition, ICPR’2000, Vol. 3: 7–12.
A. Elfes (1992). Multi-source Spatial Data Fusion Using Bayesian Reasoning. In [I]: 137–164.
A. Filippidis, L.C. Jain, N. Martin (2000). Fusion of Intelligent Agents for the Detection of Aircraft in SAR Images. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No.4: 378–384.
M. Grabisch, H.T. Nguyen and E.A. Walker (1995). Fundamentals of Uncertainty Calculi with Applications to Fuzzy Inference, Kluwer Ac. Pub.
M. Grabisch (1997). Fuzzy Measures and Integrals for Decision Making and Pattern Recognition. Fuzzy Structures: Current Trends (R. Mesiar et al. eds.), TATRA MOUNTAINS Mathematical Publications.
S.A. Hutchinson, A.C. Kak (1992). Multisensor Strategies Using Dempster-Shafer Belief Accumulation. In [I]: 165–209.
G.J. Klir, Z. Wang and D. Harmanec (1997). Constructing Fuzzy Measures in Expert systems. Fuzzy sets and Systems, 92: 251–264.
M. Köppen, C. Nowack and G. Rsel (1999). Pareto-Morphology for Color Image Processing. Proc. of the 11th Scandinavian Conference in Image Analysis, Greenland, Denmark.
M. Koppen, K. Franke, O. Unold (2000). A survey on fuzzy morphology. Proc. PRIA-5: 424–427, Samara, Russia.
R. Krishnamoorti and P. Bhattacharya (1998). Color Edge Extraction Using Orthogonal Polynomials Based Zero Crossings Scheme. Information Sciences, 112,51–65.
H. Li, B.S. Manjunath and S.K. Mitra (1995). Multisensor Image Fusion Using the Wavelet Transform. Graphical Models and Image Processing, 57(3): 235–245.
R.C. Luo and M.G. Kay eds. (1995). Multisensor Integration and Fusion for Intelligent machines and systems. Norwood, NJ: Ablex Publishing Corporation.
G. Medioni et a. (2001). Event Detection and Analysis from Video Streams. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 23, No.8: 873–889.
Y. Miyamoto et al. (1996). Development of ‘AI-VISION’ for fluidized-bed incinerator. Proc. IEEE/SICE/RSJ Int. Conf Multisensor Fusion and Integration for Intelligent Systems: 72–77.
T. Murofushi and M. Sugeno (1991). Fuzzy t-conorm integrals with respect to fuzzy measures: generalization of Sugeno integral and Choquet integral. Fuzzy Sets and Systems, 42: 57–71.
N. Nandhakumar (1994). Robust physics-based analysis of thermal and visual imagery. Journal of the Opt. Soc. Am. A, 1994: 2981–2989.
S.-C. Pei and C.-M. Cheng (1999). Color Image Processing by Using Binary Quaternion-Moment-Preserving Thresholding Technique. IEEE Trans. On Image Processing, 8(5) 614–629.
J.L. Pech-Pacheco et al. (2000). Diatom auto focusing in bright field microscopy: a comparative study. Proc. Int. Conf. Pattern Recognition, ICPR’2000, Vol. 3: 318–325. 25. l Porrill (1988). Optimal Combination and Constraints for Geometrical Sensor Data. Int. J of Robotics Research, Vol. 7, No.6: 66–77.
H. Quiu, J. Keller (1987). Multispectral image segmentation using fuzzy techniques. Proc. North American Fuzzy Information Processing Society, May 1987: 374–387.
R.A. Salinas, C. Richardson, M.A. Abidi and R.C. Gonzalez (1996). Data Fusion: Color Edge Detection and Surface Reconstruction Through Regularization. IEEE Trans. on Industrial Electronics, 43(3): 355–363.
J. Ruiz-del-Solar and A. Soria-Frisch (2000). Bio-inspired color vision for the fusion of chromatic, infrared and textural image information. Proc. 2nd International ICSC Symposium on Neural Computation NC2000: 786–792, Berlin, Germany.
A. Soria-Frisch (2000). Intelligent Localized Fusion Operators for Color Edge Detection. Proc. 12th Scandinavian Conference on Image Analysis, SCIA 2001: 177–184, Bergen, Norway.
A. Soria-Frisch (2001). A New Paradigm for Fuzzy Aggregation in Multisensory Image Processing. In Computational Intelligence: Theory and Applications. Proc. Int. Conf 7th Fuzzy Days: 59–67, Dortmund Germany.
A. Soria-Frisch and M. Koppen (2001). Fuzzy Color Morphology based on the Fuzzy Integral. In Proc. International ICSC Congress on Computational Intelligence: Methods and Applications, CIMA’2001: 732–737, Bangor, Wales, United Kingdom.
A. Soria-Frisch (2002). Avoidance of Highlights through ILFOs in Automated Visual Inspection. To appear in edited volume Fuzzy Filters for Image Processing of the International Series Studies in Fuzziness and Soft Computing, Heidelberg: Springer Verlag.
M. Sugeno (1974). Theory of Fuzzy Integral and its applications. Ph.D. thesis.
P. Sussner (2000). Observations on morphological associative memories and the kernel method. Neurocomputing 31: 167–183.
H. Tahani and J. Keller (1990). Information Fusion in Computer Vision Using the Fuzzy Integral. IEEE Trans. Systems, Man and Cybernetics, 20(3): 733–741.
H.R. Tizhoosh (1998). Fuzzy Bildverarbeitung. Heidelberg: Springer-Verlag (in german).
P.E. Trahanias, I. Pitas and A.N. Venetsanopoulus (1994). Color Image Processing. Control and Dynamic Systems, Nr. 67: Digital Image Processing. Academic Press.
Z. Wang and G.I. Klir (1992). Fuzzy Measure Theory, Plenum Press.
S. Weber (1984). ⊥-Decomposable measures and integrals for Archimidean t-conorms ⊥. J Mathematical Analysis and Applications, Vol. 101: 114–138.
P. Weckesser, R. Dillmann (1996). Sensor-Fusion of Intensity-and Laserrange-Images. Proc. IEEE/SICE/RSJ Int. Conf Multisensor Fusion and Integration for Intelligent Systems: 501–508.
R.R. Yager and A. Kelman (1996). Fusion of Fuzzy Information With Considerations for Compatibility, Partial Aggregation, and Reinforcement. Int. J of Approximate Reasoning, 15:93–122.
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Soria-Frisch, A. (2002). Soft Data Fusion in Image Processing. In: Roy, R., Köppen, M., Ovaska, S., Furuhashi, T., Hoffmann, F. (eds) Soft Computing and Industry. Springer, London. https://doi.org/10.1007/978-1-4471-0123-9_37
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DOI: https://doi.org/10.1007/978-1-4471-0123-9_37
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