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Using similarity measures for histogram comparison

Dietrich Van der Weken UGent, Mike Nachtegael UGent and Etienne Kerre UGent (2003) LECTURE NOTES IN COMPUTER SCIENCE. 2715. p.396-403
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (proceedingsPaper)
publication status
published
subject
journal title
LECTURE NOTES IN COMPUTER SCIENCE
Lect. Notes Comput. Sci.
editor
T Bilgic, Bernard De Baets UGent and P Kaynak
volume
2715
pages
396-403 pages
publisher
Springer
place of publication
Berlin, Germany
conference name
10th International-Fuzzy-Systems-Association World Congress
conference location
Istanbul, Turkey
conference start
2003-06-30
conference end
2003-07-02
Web of Science type
Article
Web of Science id
000185510700047
ISSN
0302-9743
ISBN
3-540-40383-3
language
English
UGent publication?
yes
classification
A1
id
212800
handle
http://hdl.handle.net/1854/LU-212800
date created
2004-04-27 09:10:00
date last changed
2017-01-02 09:54:12
@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},
}

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.