Comparative Study and Image Analysis of Local Adaptive Thresholding Techniques
 |
International Journal of Engineering Trends and Technology (IJETT) |  |
| © 2016 by IJETT Journal | ||
| Volume-35 Number-9 |
||
| Year of Publication : 2016 | ||
| Authors : M.Chandrakala |
||
| DOI : 10.14445/22315381/IJETT-V35P285 |
Citation
M.Chandrakala"Comparative Study and Image Analysis of Local Adaptive Thresholding Techniques", International Journal of Engineering Trends and Technology (IJETT), V35(9),423-429 May 2016. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Thresholding is a simple but effective technique for image segmentation. In this paper, a general locally adaptive thresholding methods using neighborhood processing is presented. Local adaptive techniques are more effective in eliminating both uneven lighting disturbance, noise and ghost objects. In order to demonstrate the effectiveness, locally adaptive thresholding methods namely Niblack, Sauvola, Wolf’s, Darek Bradley, Nick’s thresholding had been implemented with real world images, printed text document and hand written text document images .Threshold based segmentation mehods had been analyzed quantitatively and qualitatively.
References
[1] M. Sezgin, B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation”, J.Electronic Imaging, Vol. 13, pp. 146–165, 2004.
[2] Otsu, N., 1979. “A threshold selection method from gray-level histograms”, IEEE Transactions on Systems Man Cybernet SMC-9 (1), pp.62–66.
[3] W.Niblack, "An Introduction to Digital Image Processing", Engle wood Cliffs, N.J. Prenlice Hall, pp. 115-1 16, 1956.
[4] Sauvola, J., Seppanen, T., Haapakoski, S., and Pietikainen, M, “Adaptive document binarization”, Proc. 4th Int. Conf. On Document Analysis and Recognition, pp. 147–152, Ulm Germany, 1997.
[5] C.Wolf, J-M. Jolion, "Extraction and Recognition of Artificial Text in Multimedia Documents", Pattern Analysis and Applications, 6(4), pp. 309- 326, (2003).
[6] Bradley, D., Roth, G, “Adaptive thresholding using integral imag”, J. Graph.Tools 12(2), pp.13–21, 2007.
[7] K. Khurshid, I. Siddiqi, C. Faure, and N. Vincent, “Comparison of Niblack inspired binarization methods for ancient documents”, in 16th International conference on Document Recognition and Retrieval, Proceedings of SPIE, San Jose, Calif, USA,2009.
[8] Rimpy Garg, Prabhneet Kaur Sandhu, “A New Approach for Image Enhancement using Hybrid Threshold ”, International Journal of Engineering Trends and Technology,Vol. 4, Issue 7, pp. 3229-3232,july 2013.
[9] Yasnoff, W.A., Mui, J.K., Bacus, J.W., 1977. “Error measures for scene segmentation”,Pattern Recognition 9, pp.217–231.
[10] Hu Q, Hou Z, Nowinski WL. “Supervised range constrained thresholding”, IEEE Trans. Image. Process, 15(1), 228-240, Jan 2006
Keywords
Image thresholding, Image segmentation, window size, Misclassification Error, False Positive Rate, False Negative Rate.