Automatic Parameter Selection for Fuzzy Entropic Optimal Threshod to Enhance the Stumpy Foreground Objects in the Image Based on a Fuzziness Measure

Naga Raju C *, Pruthvi**, Siva Priya***
* Professor and Head, IT, L.B.R.College of Engg&Tech, Mylavaram.
**-*** M.Tech, L.B.R College of Engineering, Mylavaram.
Periodicity:April - June'2011
DOI : https://doi.org/10.26634/jse.5.4.1450

Abstract

An automatic parameter selection for fuzzy entropic optimal threshold to enhance the stumpy foreground objects in the images based on a fuzziness measure is presented in this paper. This work is improvement of an existing method. Using fuzzy logic, the problems involved in finding the minimum of a criterion function are avoided. Similarity between gray levels is the key to find optimal threshold to initialize the regions of gray levels which are located at the boundaries of the ROI and after that by using an index of fuzziness a similarity process is started to find the thresholding point. The advantages of the proposed method over their conventional counterparts are fourfold. First, the automatic fuzzy parameters for optimal threshold are selected. Second, the ROI is used instead of the whole image, so any irregularity outside the ROI will have no influence on estimating the threshold. Third, it provides a mechanism to handle cost differences of different types of classification error in response to practical requirements. Fourth, is by appropriately specifying the lower a upper bounds of the background proportion within the constrained gray level range, the proposed method yields substantially more robust and more reliable segmentation.

Keywords

False Negative (FN), ROI, Fuzzy Measure, Fuzzy-Entropy and Constrained Range.

How to Cite this Article?

Naga Raju C, Pruthvi and Siva Priya (2011). Automatic Parameter Selection for Fuzzy Entropic Optimal Threshod to Enhance the Stumpy Foreground Objects in the Image Based On a Fuzziness Measure. i-manager’s Journal on Software Engineering, 5(4),47-53. https://doi.org/10.26634/jse.5.4.1450

References

[1]. J.M.S. Prewitt and M.L. Mendelssohn, (1996). “The analysis of cell images, “Ann. NY Acad. Sci., Vol. 128, pp. 1035-1053.
[2]. A. Rosenfeld and P.D.L. Torre, (1983). “ Histogram concavity analysis as an aid in threshold selection,” IEEE Trans. Syst., Man, Cybern., Vol. SMC-13, No. 2, pp. 231- 235, February.
[3]. C.H. Li and C.K. Lee, (1993). “Minimum cross entropy thresholding,” Pattern Recognition., Vol.26, pp. 617-625.
[4]. Ahuja, N., and Rosefeld, A. (1978). A note, on the use of second-order gray-level statistics for threshold selection. IEEE Trans. Systems, Man, and Cybernetics, SMC-8 912), pp. 895-898.
[5]. Dulyakarn, P. Rangsanseri, Y., and Thitimajshima, P. (1999). Segmentation of multispectral images based on multithresholding. In: 2nd International Symposium on Operationalization of Remote Sensing.
[6]. Jain, R., Katsuri, R., and Schunck, B.G. (1995). Machine Vision. McGraw-Hill International Editions, pp. 234-239.
[7]. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Trans. Systems, Man, and Cybernetics, 9(1), pp. 62-66.
[8]. Richards, J.A. (1994). Remote Sensing Digital Image Analysis. Springer-Verlag Publishing Company, Inc., pp. 133-144.
[9]. Sahoo, P.K., Soltani, S., Wong, A.K.C., Chen, Y.C. (1988). A survey of thresholding techniques. Computer Vision, Graphics, and Image processing, 41, pp. 233- 260.
[10]. A.S. Pednekar and I.A. Kakadiaris, (2006). “Image segmentation based on fuzzy connectedness using dynamic weights,” IEEE Trans. Image Process, Vol. 15, No. 6, pp. 1555–1562, June.
[11]. S. Sahaphong and N. Hiransakolwong, (2007). “Unsupervised image segmentation using automated fuzzy c-means,” in Proc. IEEE Int. Conf. Computer and Information Technology, October, pp. 690–694.
[12]. O.J. Tobias, R. Seara, and F.A. P. Soares, (1996). “Automatic image segmentation using fuzzy sets,” in Proc. 38th Midwest Symp. Circuits and Systems, Vol. 2, pp. 921–924.
[13]. O. J. Tobias and R. Seara, “Image segmentation by histogram thresholding using fuzzy sets,” IEEE Trans. Image Process., vol. 11, 2002.
[14]. C. V. Jawahar, P. K. Biswas, and A. K. Ray, “Investigations on fuzzy thresholding based on fuzzy clustering,” Pattern Recognit., vol. 30, no. 10, pp. 1605–1613, 1997.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.