CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Article:Enhancement of Image Resolution by Binarization

by Aroop Mukherjee, Soumen Kanrar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 10
Year of Publication: 2010
Authors: Aroop Mukherjee, Soumen Kanrar
10.5120/1519-1942

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 },
issue_date = { November 2010 },
volume = { 10 },
number = { 10 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number10/1519-1942/ },
doi = { 10.5120/1519-1942 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:59:21.510638+05:30
%A Aroop Mukherjee
%A Soumen Kanrar
%T Article:Enhancement of Image Resolution by Binarization
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 10
%P 15-19
%D 2010
%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
  1. Abutaleb, A.S.: ‘Automatic thresholding of gray-level pictures using two-dimensional entropy’, Comput. Vis. Graph Image Process, 1989, 47, pp. 22–32
  2. 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
  3. Brink, A.: ‘Minimum spatial entropy threshold selection’, IEE Proc., Vis. Image Signal Process., 1995, 142, (3), pp. 128–132
  4. Brink, A.: ‘Using spatial information as an aid to maximum entropy image threshold selection’, Pattern Recognit. Lett., 1996, 17, (1), pp. 29–36
  5. C.A. Glasbey, An analysis of histogram-based thresholding algorithms, Graphical Models and Image Processing, 55 (1993) 532-537.
  6. Cao, L., Shi, Z.K., and Cheng, E.K.W.: ‘Fast automatic multilevel thresholding’, Electron. Lett., 2002, 38, pp.868–870
  7. 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
  8. Gonzalez and Woods, Digital image processing, 2nd Edition, prentice hall, 2002
  9. G. Leedham, S. Varma, A. Patankar, V. Govindaraju, “Separating Text and Background in Degraded Document Images” Proc. 8th IWFHR, pp. 244-249,September, 2002.
  10. 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
  11. Haralick, R.M., and Shapiro, L.G.: ‘Computer and robot vision, vol. 1’ (Addison-Wesley, 1992)
  12. J.S. Weszka, A. Rosenfeld, Threshold evaluation techniques, IEEE Trans. Systems, Man and Cybernetics, SMC-8(8) (1978) 627-629.
  13. 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.
  14. 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
  15. 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
  16. M. Sezgin, B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation”, Journal of Electronic Imaging 13(1), 146–168, 2004.
  17. Otsu, N.: ‘A thresholding selection method from gray- level histogram’, IEEE Trans. Syst. Man Cybern., 1979, 9, (1), pp. 62–66
  18. O.D.Trier, A.K. Jain, Goal-directed evaluation of binarization methods, IEEE Tran. Pattern Analysis and Machine Intelligence, PAMI-17 (1995) 1191-1201
  19. Pal, N.R., and Pal, S.K.: ‘A new definition and its application’, IEEE Trans. Syst. Man Cybern., 1991, 21, (5), pp. 1260– 1270
  20. 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
  21. Papamarkos, N., and Atsalakis, A.: ‘Gray-level reduction using local spatial features’, Comput. Vis Image Underst., 2000, 78, pp. 336–350
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. Trier, O., and Taxt, T.: ‘Evaluation of binarisation methods for document images’, IEEE Trans. Pattern Anal. Mach. Intell., 1995, 17, pp. 312–315.
  30. 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

Keywords

Thresholding Binarization Optimum Threshold Mean Value

Powered by PhDFocusTM