Abstract
A good reference image is important for relative performance analysis of different image thresholding techniques in a quantitative manner. There exist standard methods for building reference images for document image binarization. However, a gap is found for graphic images referencing. This paper offers six different techniques for building reference images. These may be used for comparing different image thresholding techniques. Experimental results illustrate the relative performance of five different image thresholding methods for the six reference image building methods on a set of ten images taken from the USC-SIPI database. The results would help picking up the right reference image for evaluating binarization techniques.
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References
Shaikh, S.H., Maiti, H.A., Chaki, N.: A New Image Binarization Method using Iterative Partitioning. Springer Journal on Machine Vision and Applications (revised manuscript submitted in July 2011) ISSN: 0932-8092
Rodriguez, R.: A Robust Algorithm for Binarization of Objects. Latin American Applied Research 40 (2010)
Rodriguez, R.: Binarization of Medical Images based on the Recursive Application of Mean Shift Filtering: Another Algorithm. In: Advances and Applications in Bioinformatics and Chemistry (2008)
Ntirogiannis, K., Gatos, B., Pratikakis, I.: An Objective Evaluation Methodology for Document Image Binarization Techniques. In: 8th IAPR Workshop on Document Analysis Systems (2008)
Sezgin, M., Sankur, B.: Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. Journal of Electronic Imaging 13(1), 146–165 (2004)
Sauvola, J., Pietikainen, M.: Adaptive Document Image Binarization. Pattern Recognition 33(2), 225–236 (2000)
Yang, Y., Yan, H.: An Adaptive Logical Method for Binarization of Degraded Document Images. Pattern Recognition 33, 787–807 (2000)
Savakis, E. A.: Adaptive Document Image Thresholding using Foreground and Background Clustering. In: Int. Conf. on Image Processing (ICIP 1998), Chicago (October 1998)
Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB, 2nd edn., ch. 10, p. 513. McGrawHill
Niblack, W.: An Introduction to Digital Image Processing, pp. 115–116. Prentice Hall, Eaglewood Cliffs (1986)
Bernsen, J.: Dynamic Thresholding of Gray Level Images. In: ICPR 1986: Proc. Intl. Conf. Patt. Recog., pp. 1251–1255 (1986)
Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A New Method for Gray-level Picture Thresholding using the Entropy of the Histogram. In: Graph. Models Image Process., pp. 273–285 (1985)
Otsu, N.: A Threshold Selection Method from Gray-Level Histogram. IEEE Transactions on Systems, Man, and Cybernetics 9, 62–66 (1979)
University of Southern California, Signal and Image Processing Institute, USC-SIPI Image Database, http://sipi.usc.edu/database/
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Shaikh, S.H., Maiti, A.K., Chaki, N. (2011). On Creation of Reference Image for Quantitative Evaluation of Image Thresholding Method. In: Chaki, N., Cortesi, A. (eds) Computer Information Systems – Analysis and Technologies. Communications in Computer and Information Science, vol 245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27245-5_21
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DOI: https://doi.org/10.1007/978-3-642-27245-5_21
Publisher Name: Springer, Berlin, Heidelberg
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