Abstract
Objective quality measures or measures of comparison are of great importance in the field of image processing. Such measures are needed for the evaluation and the comparison of different algorithms that are designed to solve a similar problem, and consequently they serve as a basis on which one algorithm is preferred above the other. Similarity measures, originally introduced to compare two fuzzy sets, can be applied in different ways to images. In [2] we gave an overview of similarity measures which can be applied straightforward to images. In this paper, we will show how some similarity measures can be applied to normalized histograms of images.
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References
D. Van der Weken, M. Nachtegael, and E.E. Kerre, The applicability of similarity measures in image processing. To appear in Proceedings of the 8th International Conference on Intelligent Systems and Computer Sciences (December 4–9, 2000, Moscow, Russia); in Russian.
D. Van der Weken, M. Nachtegael, and E.E. Kerre, An overview of similarity measures for images. Proceedings of ICASSP’2002 (IEEE International Conference on Acoustics, Speech and Signal Processing), Orlando, United States, 2002, pp. 3317–3320.
D. Van der Weken, M. Nachtegael, and E.E. Kerre, A new similarity measure for image processing. Journal of Computational Methods in Sciences and Engineering, vol.3, no.2, 2003, pp.17–30.
D. Van der Weken, M. Nachtegael, and E.E. Kerre, Image comparison using inclusion measures. In preparation.
D. Van der Weken, M. Nachtegael, and E.E. Kerre, Similarity Measures in Image Processing. Internal Research Report (136 pages); in Dutch.
D. Van der Weken, M. Nachtegael, and E.E. Kerre, Image comparison through application of similarity measures to fuzzy sets associated with the histograms of the images. Internal Research Report (68 pages); in Dutch.
M. Nachtegael, D. Van der Weken, D. Van De Ville, E.E. Kerre, W. Philips, I. Lemahieu, A comparative study of classical and fuzzy filters for noise reduction. Proc. of FUZZ-IEEE’2001, pp. 11–14 (CD), 2001.
C. Cornelis, C. Vanderdonck, and E.E. Kerre, Sinha-Dougherty approach to the fuzzification of set inclusion revisited. Fuzzy Sets and Systems, in press.
B. De Baets, and H. De Meyer, The Frank T-norm family in fuzzy similarity measurement. Proc. of EUSFLAT’2002, 2nd Conference on the European Society for Fuzzy Logic and Technology, September 5–7, 2001, Leicester (England), pp. 249–252.
L.A. Zadeh, The role of fuzzy logic in the management of uncertainty in expert systems. Selected papers by L.A. Zadeh, R.R. Yager (editor), New York, Wiley, pp.413–441.
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Van der Weken, D., Nachtegael, M., Kerre, E. (2003). Using Similarity Measures for Histogram Comparison. In: Bilgiç, T., De Baets, B., Kaynak, O. (eds) Fuzzy Sets and Systems — IFSA 2003. IFSA 2003. Lecture Notes in Computer Science, vol 2715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44967-1_47
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DOI: https://doi.org/10.1007/3-540-44967-1_47
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