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Cross-Entropy Based Image Thresholding

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a novel global thresholding algorithm for the binarization of documents and gray-scale images using Cross-Entropy Clustering. In the first step, a gray-level histogram is constructed, and the Gaussian densities are fitted. The thresholds are then determined as the cross-points of the Gaussian densities. This approach automatically detects the number of components (the upper limit of Gaussian densities is required).
Słowa kluczowe
Rocznik
Tom
Strony
21--29
Opis fizyczny
Bibliogr. 20 poz., rys.
Twórcy
autor
  • Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland
autor
  • Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland
autor
  • Faculty of Mathematics and Computer Science Jagiellonian University, ul. Łojasiewicza 6, 30-348 Kraków, Poland
Bibliografia
  • [1] Gonzalez R.C., Woods R.E., Digital image processing, Prentice-Hall, Inc., Upper Saddle River, New Jersey, United States of America, 2002.
  • [2] Huang D.S., Systematic theory of neural networks for pattern recognition. Publishing House of Electronic Industry of China, Beijing, 1996, 201.
  • [3] Tabor J., Spurek P., Cross-entropy clustering. Pattern Recognition, 2014, 47(9), pp. 3046–3059.
  • [4] Cover T.M., Thomas J.A., Elements of information theory, John Wiley & Sons, New York, United States of America, 2012.
  • [5] Gr¨unwald P.D., The minimum description length principle. MIT Press, Cambridge, Massachusetts, United States of America, 2007.
  • [6] Otsu N., A threshold selection method from gray-level histograms. Automatica, 1975, 11(285–296), pp. 23–27.
  • [7] Liu Y., Srihari S.N., Document image binarization based on texture features. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1997, 19(5), pp. 540–544.
  • [8] Prewitt J., Mendelsohn M.L., The analysis of cell images. Annals of the New York Academy of Sciences, 1966, 128(3), pp. 1035–1053.
  • [9] Doyle W., Operations useful for similarity-invariant pattern recognition. Journal of the ACM (JACM), 1962, 9(2), pp. 259–267.
  • [10] Johannsen G., Bille J., A threshold selection method using information measures. Proc. Sixth Int. Conf. Pattern Recognition, 1982, pp. 140–143.
  • [11] Kapur J.N., Sahoo P.K., Wong A.K.C., A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision, Graphics, and Image Processing, 1985, 29, pp. 273–285.
  • [12] Ridler T.W., Calvard S., Picture thresholding using an iterative selection method. IEEE transactions on Systems, Man and Cybernetics, 1978, 8(8), pp. 630–632.
  • [13] Tsai W.H., Moment-preserving thresolding: A new approach. Computer Vision, Graphics, and Image Processing, 1985, 29(3), pp. 377–393.
  • [14] Kittler J., Illingworth J., On threshold selection using clustering criteria. Systems, Man and Cybernetics, IEEE Transactions on, 1985, 5, pp. 652–655.
  • [15] Friedman N., Russell S., Image segmentation in video sequences: A probabilistic approach. In: Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., 1997, pp. 175–181.
  • [16] Huang Z.K., Chau K.W., A new image thresholding method based on Gaussian mixture model. Applied Mathematics and Computation, 2008, 205(2), pp. 899–907.
  • [17] Papamarkos N. B. G., A new approach for multilevel threshold selection. Graphical Models and Image Processing, 1994, 56, pp. 357–370.
  • [18] Mehmet S., Sankur B., Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging, 2004, 13, pp. 146–165.
  • [19] Matthews B., Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochimica et Biophysica Acta (BBA) – Protein Structure, 1975, 405, pp. 442–451.
  • [20] Gatos B., Ntirogiannis K., Pratikakis I., ICDAR 2009 document image binarization contest (DIBCO 2009). In: Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference on, IEEE, 2009, pp. 1375–1382.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-56148762-49bb-43fe-b8b7-d375b77019f0
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