Research Article

Article:Enhancement of Image Resolution by Binarization

by  Aroop Mukherjee, Soumen Kanrar
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Issue 10
Published: November 2010
Authors: Aroop Mukherjee, Soumen Kanrar
10.5120/1519-1942
PDF

Aroop Mukherjee, Soumen Kanrar . Article:Enhancement of Image Resolution by Binarization. International Journal of Computer Applications. 10, 10 (November 2010), 15-19. DOI=10.5120/1519-1942

                        @article{ 10.5120/1519-1942,
                        author  = { Aroop Mukherjee,Soumen Kanrar },
                        title   = { Article:Enhancement of Image Resolution by Binarization },
                        journal = { International Journal of Computer Applications },
                        year    = { 2010 },
                        volume  = { 10 },
                        number  = { 10 },
                        pages   = { 15-19 },
                        doi     = { 10.5120/1519-1942 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2010
                        %A Aroop Mukherjee
                        %A Soumen Kanrar
                        %T Article:Enhancement of Image Resolution by Binarization%T 
                        %J International Journal of Computer Applications
                        %V 10
                        %N 10
                        %P 15-19
                        %R 10.5120/1519-1942
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation is one of the principal approaches of image processing. The choice of the most appropriate Binarization algorithm for each case proved to be a very interesting procedure itself. In this paper, we have done the comparison study between the various algorithms based on Binarization algorithms and propose a methodologies for the validation of Binarization algorithms. In this work we have developed two novel algorithms to determine threshold values for the pixels value of the gray scale image. The performance estimation of the algorithm utilizes test images with, the evaluation metrics for Binarization of textual and synthetic images. We have achieved better resolution of the image by using the Binarization method of optimum thresholding techniques.

References
  • Abutaleb, A.S.: ‘Automatic thresholding of gray-level pictures using two-dimensional entropy’, Comput. Vis. Graph Image Process, 1989, 47, pp. 22–32
  • Alginahi, Y., Sid-Ahmed, M.A., and Ahmadi, M.: ‘Local thresholding of composite documents using multi-layer preceptron neural network’. Proc. 2004 IEEE Midwest Symp. on Circuits and Systems, July 2004, Hiroshima, Japan, pp. 209–212
  • Brink, A.: ‘Minimum spatial entropy threshold selection’, IEE Proc., Vis. Image Signal Process., 1995, 142, (3), pp. 128–132
  • Brink, A.: ‘Using spatial information as an aid to maximum entropy image threshold selection’, Pattern Recognit. Lett., 1996, 17, (1), pp. 29–36
  • C.A. Glasbey, An analysis of histogram-based thresholding algorithms, Graphical Models and Image Processing, 55 (1993) 532-537.
  • Cao, L., Shi, Z.K., and Cheng, E.K.W.: ‘Fast automatic multilevel thresholding’, Electron. Lett., 2002, 38, pp.868–870
  • Chang, C.C., Chang, C.H., and Hwang, S.Y.: ‘A connectionist approach for thresholding’. Proc. Int. Conf. Pattern Recognit., 1992, vol. 11, pp. 522–525
  • Gonzalez and Woods, Digital image processing, 2nd Edition, prentice hall, 2002
  • G. Leedham, S. Varma, A. Patankar, V. Govindaraju, “Separating Text and Background in Degraded Document Images” Proc. 8th IWFHR, pp. 244-249,September, 2002.
  • H C Sateesh Kumar, K B Raja, Venugopal K R and L M Patnaik “Automatic Image Segmentation using Wavelets”, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.2, February 2009
  • Haralick, R.M., and Shapiro, L.G.: ‘Computer and robot vision, vol. 1’ (Addison-Wesley, 1992)
  • J.S. Weszka, A. Rosenfeld, Threshold evaluation techniques, IEEE Trans. Systems, Man and Cybernetics, SMC-8(8) (1978) 627-629.
  • J. He, Q.D.M. Do, A.C. Downton, J.H. Kim, “A Comparison of Binarization Methods for Historical Archive Documents”, Proc. 8th ICDAR, pp. 538-542, 2005.
  • Kapur, J.N., Sahoo, P.K., and Wong, A.K.C.: ‘A new method for gray-level picture thresholding using the entropy of the histogram’, Comput. Vis. Graph. Image Process, 1985, 29, (3), pp. 273–285
  • Lee, S.U., Chung, S.Y., and Park, R.H.: ‘A comparative performance study of several global thresholding techniques for segmentation’, Comput. Vis. Graph Image Process. 1990, 52, pp. 171–190
  • M. Sezgin, B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic Imaging 13(1), 146–168, 2004.
  • Otsu, N.: ‘A thresholding selection method from gray- level histogram’, IEEE Trans. Syst. Man Cybern., 1979, 9, (1), pp. 62–66
  • O.D.Trier, A.K. Jain, Goal-directed evaluation of binarization methods, IEEE Tran. Pattern Analysis and Machine Intelligence, PAMI-17 (1995) 1191-1201
  • Pal, N.R., and Pal, S.K.: ‘A new definition and its application’, IEEE Trans. Syst. Man Cybern., 1991, 21, (5), pp. 1260– 1270
  • Pal, N.R., and Pal, S.K.: ‘Object-background segmentation using new definitions of entropy’, IEEE Proc., Comput. Digital Tech., 1989, 136, (4), pp. 284–295
  • Papamarkos, N., and Atsalakis, A.: ‘Gray-level reduction using local spatial features’, Comput. Vis Image Underst., 2000, 78, pp. 336–350
  • Papamarkos, N., and Gatos, B.: ‘A new approach for multithreshold selection’, Comput. Vis. Graph. Image Process. Graph. Models Image Process, 1994, 56, (5), pp. 357–370
  • Papamarkos, N., Strouthopoulos, C., and Andreadis, I.: ‘Multithresholding of color and gray-level images through a neural network technique’, Image Vis. Comput., 2000, 18, pp. 213–222
  • P.W. Palumbo, P. Swaminathan, S.N. Srihari, Document image binarization: Evaluation of algorithms Proc. SPIE Applications of Digital Image Proc., SPIE Vol. 697, (1986), pp:278-286
  • Reddi, S., Rudin, S., and Keshavan, H.: ‘An optimal multiple threshold scheme for image segmentation’, IEEE Trans. Syst. Man Cybern., 1984, 14, pp. 661– 665
  • Sahoo, P.K., Slaaf, D.W., and Albert, T.A.: ‘Threshold selection using a minimal histogram entropy difference’,Opt. Eng., 1997, 36, pp. 1976–1981
  • Sahoo, P.K., Soltani, S., Wong, A.K.C., and Chen, Y.C.: ‘A survey of thresholding techniques’, Comput. Vis Graph. Image Process. 1988, 41, pp. 233–260
  • T. Ridler and S. Calvard, “Picture thresholding using an iterative selection method,” IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 8, pp. 630– 632, Aug. 1978
  • Trier, O., and Taxt, T.: ‘Evaluation of binarisation methods for document images’, IEEE Trans. Pattern Anal. Mach. Intell., 1995, 17, pp. 312–315.
  • Tsai, D.M.: ‘A fast thresholding selection procedure for multimodal and unimodal histograms’, Pattern Recognit. Lett., 1995, 16, (6), pp. 653–666
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Thresholding Binarization Optimum Threshold Mean Value

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