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Using similarity measures for histogram comparison

Dietrich Van der Weken (UGent) , Mike Nachtegael (UGent) and Etienne Kerre (UGent)
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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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Chicago
Van der Weken, Dietrich, Mike Nachtegael, and Etienne Kerre. 2003. “Using Similarity Measures for Histogram Comparison.” Ed. T Bilgic, Bernard De Baets, and P Kaynak. Lecture Notes in Computer Science 2715: 396–403.
APA
Van der Weken, Dietrich, Nachtegael, M., & Kerre, E. (2003). Using similarity measures for histogram comparison. (T. Bilgic, B. De Baets, & P. Kaynak, Eds.)LECTURE NOTES IN COMPUTER SCIENCE, 2715, 396–403. Presented at the 10th International-Fuzzy-Systems-Association World Congress.
Vancouver
1.
Van der Weken D, Nachtegael M, Kerre E. Using similarity measures for histogram comparison. Bilgic T, De Baets B, Kaynak P, editors. LECTURE NOTES IN COMPUTER SCIENCE. Berlin, Germany: Springer; 2003;2715:396–403.
MLA
Van der Weken, Dietrich, Mike Nachtegael, and Etienne Kerre. “Using Similarity Measures for Histogram Comparison.” Ed. T Bilgic, Bernard De Baets, & P Kaynak. LECTURE NOTES IN COMPUTER SCIENCE 2715 (2003): 396–403. Print.
@article{212800,
  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.},
  author       = {Van der Weken, Dietrich and Nachtegael, Mike and Kerre, Etienne},
  editor       = {Bilgic, T and De Baets, Bernard and Kaynak, P},
  isbn         = {3-540-40383-3},
  issn         = {0302-9743},
  journal      = {LECTURE NOTES IN COMPUTER SCIENCE},
  language     = {eng},
  location     = {Istanbul, Turkey},
  pages        = {396--403},
  publisher    = {Springer},
  title        = {Using similarity measures for histogram comparison},
  volume       = {2715},
  year         = {2003},
}

Web of Science
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