Histogram Thresholding using Beam Theory and Ambiguity Measures

• Autorzy: Sen D. , Pal S.K.
• Języki publikacji: EN

Abstrakty

EN

This paper presents a novel histogram thresholding technique based on the beam theory of solid mechanics and the minimization of ambiguity in information. First, a beam theory based histogram modification process is carried out. This beam theory based process considers a distance measure in order to modify the shape of the histogram. The ambiguity in the overall information given by the modified histogram is then minimized to obtain the threshold value. The ambiguity minimization is carried out using the theories of fuzzy and rough sets, where a new definition of rough entropy is presented. The applications of the proposed scheme in performing object and edge extraction in images are reported and compared with those of a few existing classical and ambiguity minimization based schemes for thresholding. Experimental results are given to demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative measures.

Słowa kluczowe

EN

histogram thresholding; histogram modification; ambiguity minimization; fuzzy sets; rough sets; beam theory; object extraction; edge extraction

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2007
• Tom: Vol. 75, nr 1-4
• Strony: 483--504
• Opis fizyczny: bibliogr. 29 poz., fot., tab.,wykr.

Twórcy

autor

Sen D.

autor

Pal S.K.

• Center for Soft Computing Research. Indian Statistical Institute, 203 B.T. Road, Kolkata, West Bengal, India 700108,

Bibliografia

• [1] Abdou, I. E., Pratt, W. K.: Quantitative design and evaluation of enhancement/thresholding edge detectors, Proc. IEEE, 67(5), 1979, 753-763.
• [2] Audoly, B., Neukirch, S.: Fragmentation of rods by cascading cracks: why spaghetti do not break in half, Physical Review Letters, 95, 2005, 095505 1-4.
• [3] Canny, J.: A computational approach to edge detection, IEEE Trans. Pattern Anal. Machine Intell., 8(6), 1986, 679-698.
• [4] Cheng, H. D., Jiang, X. H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects, Pattern Recognition, 34(12), 2001, 2259-2281.
• [5] Kanade, T.: Image understanding research at CMU, Proceedings of Image Understanding Workshop, II, 1987.
• [6] Klir, G., Yuan, B.: Fuzzy sets and fuzzy logic: theory and applications, Prentice Hall, New Delhi, India, 2005.
• [7] Lee, S. U., Chung, S. Y., Park, R. H.: A comparative performance study of several global thresholding techniques for segmentation, Computer Vision, Graphics and Image Processing, 52(2), 1990, 171-190.
• [8] Murthy, C. A., Pal, S. K.: Histogram thresholding by minimizing graylevel fuzziness, Information Sciences, 60(1-2), 1992, 107-135.
• [9] Nayfeh, A. H., Pai, P. F.: Linear and nonlinear structural mechanics, Wiley Series in Nonlinear Science, John Wiley & Sons, New York, USA, 2004.
• [10] Otsu, N.: A threshold selection method from gray-level histogram, IEEE Trans. Syst., Man, Cybern., 9(1), 1979, 62-66.
• [11] Pal, N. R., Bezdek, J. C.: Measuring Fuzzy Uncertainity, IEEE Trans. Fuzzy Syst., 2(2), 1994, 107-118.
• [12] Pal, N. R., Pal, S. K.: A review on image segmentation techniques, Pattern Recognition, 26(9), 1993, 1277-1294.
• [13] Pal, S. K., Ghosh, A.: Index of area coverage of fuzzy image subsets and object extraction, Pattern Recognition Letters, 11(12), 1990, 831-841.
• [14] Pal, S. K., Ghosh, A.: Fuzzy geometry in image analysis, Fuzzy Sets and Systems, 48(1), 1992, 23-40.
• [15] Pal, S. K., Ghosh, A.: Image segmentation using fuzzy correlation, Information Sciences, 62(3), 1992, 223-250.
• [16] Pal, S. K., King, R. A., Hashim, A. A.: Automatic grey level thresholding through index of fuzziness and entropy, Pattern Recognition Letters, 1(3), 1983, 141-146.
• [17] Pal, S. K., Rosenfeld, A.: Image enhancement and thresholding by optimization of fuzzy compactness, Pattern Recognition Letters, 7(2), 1988, 77-86.
• [18] Pal, S. K., Shankar, B. U., Mitra, P.: Granular computing, rough entropy and object extraction, Pattern Recognition Letters, 26(16), 2005, 2509-2517.
• [19] Pawlak, Z.: Rough sets: theoritical aspects of reasoning about data, Kluwer Academic, Dordrecht, Netherlands, 1991.
• [20] Prasad, I. B.: Applied mechanics and strength of materials, 5th edition, Khanna Publishers, Delhi, India, 1983.
• [21] Pun, T.: Entropic thresholding: a new approach, Computer Graphics and Image Processing, 16, 1981, 210-239.
• [22] Rosen¯feld, A., Torre, P. D. L.: Histogram concavity analysis as an aid in threshold selection, IEEE Trans. Syst., Man, Cybern., 13(3), 1983, 231-235.
• [23] Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quatitative performance evaluation, Journal of Electronic Imaging, 13(1), 2004, 146-168.
• [24] Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems, Slowiński, R., (Ed.), Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, 1992.
• [25] Timoshenko, S.: Strength of materials, 3rd edition, Nostrand, London, England, 1958.
• [26] Tobias, O. J., Seara, R.: Image segmentation by histogram thresholding using fuzzy sets, IEEE Trans. Image Processing, 11(12), 2002, 1457-1465.
• [27] Tsai, W.-H.: Moment-preserving thresholding: a new approach, Computer Vision, Graphics, and Image Processing, 29, 1985, 377-393.
• [28] Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P.: Image quality assesment: from error visibility to structural similarity, IEEE Trans. Image Processing, 13(4), 2004, 600-612.
• [29] Wezka, J. S., Rosenfeld, A.: Histogram modification for threshold selection, IEEE Trans. Syst., Man, Cybern., 9(1), 1979, 38-52.