A Survey on Image Segmentation Using Threshoding Methods
Shubham Arjariya, Dr. Mahesh Motwani, Dr. SHIKHA AGRAWAL "A Survey on Image Segmentation Using Threshoding Methods". International Journal of Computer Trends and Technology (IJCTT) V41(2):59-66, November 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Image segmentation basically divided into two types as: based on similarity and based on discontinuity. Region based segmentation is a type similarity based segmentation. Another type of segmentation is called thresholding based segmentation. In thresholding based segmentation method some thresholding techniques are used. Thresholding techniques are classified into two major categories as: Global and Local. In global thresholding, pixel values are categorized into two classes, one class belongs to object and another class belongs to background. We use one threshold value in global thresholding for whole image that belongs to single level thresholding and if threshold value used in segmentation is more than one, technique is called multilevel thresholding. In this paper we have compared to global and local thresholding method.
References
[1] S. Jayaraman, S. Esakkirajan, T. Veerakumar, “Digital Image Processing,” Tata McGraw Hill Education Private Limited, 2009.
[2] Puneet and Naresh Kumar Garg, “Binarization Techniques used for Grey Scale Images,” International Journal of Computer Applications (0975 – 8887) Vol. 71, No.1, June 2013.
[3] Savita Agrawal, Deepak Kumar Xaxa, “Survey on Image Segmentation Techniques and Color Models,” International Journal of Computer Science and Information Technologies, ISSN: 0975-9640, Vol. 5 (3), 2014.
[4] A. M. Khan, Ravi. S, “Image Segmentation Methods: A Comparative Study,” International Journal of Soft Computing and Engineering (IJSCE) ISSN:2231-2307, Vol.3, Issue 4, September 2013.
[5] V. Sivakumar and V.Murugesh, “A Brief Study of Image Segmentation using Thresholding Technique on a Noisy Image,” IEEE, 2014.
[6] Y. Nakagawa and A. Rosenfeld, “Some experiments on variable thresholding, Pattern Recognition,” Vol. 11, 1979, pp. 191-204.
[7] K. K. Singh, A. Singh, “A Study Of Image Segmentation Algorithms for Different Types Of Images,” IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010.
[8] S.S. Al-amri, N.V. Kalyankar and Khamitkar S.D, “Image Segmentation by Using Threshold Techniques,” Journal of Computing, Vol. 2, Issue 5, May 2010.
[9] Mantas Paulinas, Andrius Ušinskas, “A Survey of Genetic Algorithms Applications for Image Enhancement and Segmentation” ISSN 1392-124X, Information Technology and Control, Vol.36, No.3, 2007, pp. 278-284.
[10] N. Senthilkumaran and R. Rajesh, “Edge Detection Techniques for Image Segmentation– A Survey of Soft Computing Approaches,” International Journal of Recent Trends in Engineering, Vol. 1, No. 2, May 2009.
[11] Sanjay Agrawal, Rutuparna Panda and Lingraj Dora, “A study on fuzzy clustering for magnetic resonance brain image segmentation using soft computing approaches,” Applied Soft Computing 24, 2014, pp. 522–533.
[12] Jayaram K., Udupa And Supun Samarasekera, “Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation,” Graphical Models and Image Processing Vol. 58, No. 3, May 1996, Article No. 0021, pp. 246–261.
[13] L.A. Zadeh, “Some reflections on soft computing, granular Computing and their roles in the conception, design and utilization of information/intelligent systems,” Soft Computing, Vol.2, 1998, pp.23-25.
[14] Mantas Paulinas, Andrius Ušinskas, “A Survey of Genetic Algorithms Applications for Image Enhancement and Segmentation,” ISSN 1392-124x Information Technology And Control, Vol.36, No.3 2007, pp. 278-284.
[15] M.S.Makesar, Dr.N.A.Koli, R.N.Khobragade, “Evolutionary Based Segmentation in Image Mining,” International Journal of Innovative Research in Computer and Communication Engineering.
[16] Kohonen, T., “The Self-organizing Maps,” third ed. Springer, Germany (2000).
[17] Kostas Haris, Serafim N. Efstratiadis, Nicos Maglaveras, Aggelos K. Katsaggelos, “Hybrid Image Segmentation Using Watersheds and Fast Region Merging,” IEEE Transactions On Image Processing, Vol. 7, No. 12, December 1998, pp. 1664-1699.
[18] A. Kanchan Deshmukh, B. Ganesh Shinde, “Adaptive Color Image Segmentation Using Fuzzy Min-Max Clustering,” Engineering Letters, 13:2, EL_13_2_2 Advance online publication.
[19] Kinjal S Patel and Neha D Parmar, “Unsupervised Multi-Spectral Based Image Segmentation and Supervised Based Image Segmentation Technique,” IJARCSMS Vol. 3, Issue 2, February 2015.
[20] Tim Vitale, “Digital Image File Formats and their Storage -- TIFF, JPEG & JPEG2000,” Vol. 20, Feb, 2010.
[21] Salem Saleh Al-amri, N.V. Kalyankar and Khamitkar S.D, “Image Segmentation by using Thershod Techniques,” Journal of Computing, Vol. 2, 2010, pp. 83-86.
[22] R. C. Gonzalez and R. E. Woods, Digital Image Processing (Prentice Hall, 2002).
[23] Dibya Jyoti Bora, Anil Kumar Gupta and Fayaz Ahmad Khan, “Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation,” ISSN 2250-2459, Vol. 5, Issue 2, FEBRUARY 2015, pp. 192-204.
[24] K. Bhargavi, S. Jyothi, “A Survey on Threshold Based Segmentation Technique in Image Processing,” IJIRD Vol. 3, Issue 12, November, 2014.
[25] Hari Kumar Singh, Shiv Kumar Tomar and Prashant Kumar Maurya, “Thresholding Techniques applied for Segmentation of RGB and multispectral images,” Proceedings published by International Journal of Computer Applications 2012.
[26] Dr. Vipul Singh, “Digital Image Processing With Matlab and Lab View,” Elsevier 2013.
[27] T. Ridler and C.Calvard, “Thresholding using an Iterative Selection Methods,” IEEE, Transaction on Systems, Vol. SMC-8, 1978, pp. 630-632.
[28] S. Jayaraman, S. Esakkirajan, T. Veerakumar, “Digital Image Processing,” Tata McGraw Hill Education Private Limited, 2009.
[29] A.S. Abutaleb, “Automatic Thresholding of Gray-Level Pictures using Two Dimensional Entropy,” Computer Vision, Graphics, and Image processing, Vol.47, 1989, pp. 22-32.
Keywords
Image Segmentation, Thresholding, Local Thresholding, Global Thresholding.