Open Access   Article Go Back

Segmentation Using Fuzzy Membership Functions: An Approach

E. B. Kumar1 , V. Thiagarasu2

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-3 , Page no. 101-105, Mar-2017

Online published on Mar 31, 2017

Copyright © E. B. Kumar, V. Thiagarasu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: E. B. Kumar, V. Thiagarasu, “Segmentation Using Fuzzy Membership Functions: An Approach,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.101-105, 2017.

MLA Style Citation: E. B. Kumar, V. Thiagarasu "Segmentation Using Fuzzy Membership Functions: An Approach." International Journal of Computer Sciences and Engineering 5.3 (2017): 101-105.

APA Style Citation: E. B. Kumar, V. Thiagarasu, (2017). Segmentation Using Fuzzy Membership Functions: An Approach. International Journal of Computer Sciences and Engineering, 5(3), 101-105.

BibTex Style Citation:
@article{Kumar_2017,
author = {E. B. Kumar, V. Thiagarasu},
title = {Segmentation Using Fuzzy Membership Functions: An Approach},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2017},
volume = {5},
Issue = {3},
month = {3},
year = {2017},
issn = {2347-2693},
pages = {101-105},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1217},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1217
TI - Segmentation Using Fuzzy Membership Functions: An Approach
T2 - International Journal of Computer Sciences and Engineering
AU - E. B. Kumar, V. Thiagarasu
PY - 2017
DA - 2017/03/31
PB - IJCSE, Indore, INDIA
SP - 101-105
IS - 3
VL - 5
SN - 2347-2693
ER -

VIEWS PDF XML
781 389 downloads 438 downloads
  
  
           

Abstract

This article presents a novel approach for color image segmentation using two different algorithms with respect to color features. Color Image Segmentation separates the image into distinct regions of similar pixels based on pixel property. It is the high level image description in terms of objects, scenes, and features. The success of image analysis depends on segmentation reliability. Here presented an adaptive masking method based on fuzzy membership functions and a thresholding mechanism over each color channel to overcome over segmentation problem, before combining the segmentation from each channel into the final one. Our proposed method ensures accuracy and quality of different kinds of color images. Consequently, the proposed modified fuzzy approach can enhance the image segmentation performance by use of its membership functions. Similarly, it is worth noticing that our proposed approach is faster than many other segmentation algorithms, which makes it appropriate for real-time application. According to the visual and quantitative verification, the proposed algorithm is performing better than existing algorithms.

Key-Words / Index Term

Segmentation, Fuzzy Membership Functions, Fuzzy Inference System, Edge Detection, Region Growing and Thresholding

References

[1] Rafael C Gonzalez and Richard E Woods, “Digital image processing”, 2013, Pearson Education, ISBN 978-81-317-2695-2.
[2] Rafael C Gonzalez, Richard E Woods and Steven L Eddins, “Digital image processing using MATLAB”, 2011, Tata McGraw Hill Education , ISBN – 13: 978-0-07-070262-2.
[3] Firas Ajil Jassim, Fawzi H. Altaani, “Hybridization of Otsu Method and Median Filter for Color Image Segmentation”, International Journal of Soft Computing and Engineering, Vol. 3(2), 2013.
[4] Song Gao, Chengcui Zhang, and Wei-Bang Chen, “An Improvement of Color Image Segmentation through Projective Clustering”, IEEE, 2012.
[5] Mrs. C. Mythili, Dr. V. Kavitha, “Efficient Technique for Color Image Noise Reduction”, The Research Bulletin of Jordan, ACM, Vol. 2(3), 2011.
[6] Nikita Sharma, Mahendra Mishra, Manish Shrivastava, “Color Image Segmentation Techniques and Issues: An Approach”, International Journal of Scientific & Technology Research Vol. 1(4), 2012.
[7] Jan Puzicha and Serge Belongie, “Model–based Halftoning for Color Image Segmentation”, UC Berkeley, Department of Computer Science, 2002.
[8] Soumya Dutta, Bidyut B. Chaudhuri, “Homogenous Region based Color Image Segmentation”, Proceedings of the World Congress on Engineering and Computer Science, Vol. 2, 2009.
[9] E. Boopathi Kumar and M. Sundaresan, “Edge Detection Using Trapezoidal Membership Function Based on Fuzzy‘s Mamdani Inference System”, IEEE, 2014.
[10] E. Boopathi Kumar and M. Sundaresan, “Fuzzy Inference System based Edge Detection using Fuzzy Membership Functions”, International Journal of Computer Applications, Vol. 112(4), 2015.
[11] M. Jogendra Kumar, Dr. GVS Raj Kumar, R. Vijay Kumar Reddy, “Review on Image Segmentation Techniques”, International Journal of Scientific Research Engineering & Technology, Vol. 3(6), 2014.
[12] Rozy Kumari, Narinder Sharma, “A Study on the Different Image Segmentation Technique”, International Journal of Engineering and Innovative Technology, Vol. 4(1), 2014.
[13] H.D. Cheng, X.H. Jiang, Y. Sun, Jingli Wang, “Color image segmentation: advances and prospects”, Pattern Recognition, pp.2259-2281, 2001.
[14] M. Borsotti, P. Campadelli, R. Schettini, “Quantitative evaluation of color image segmentation results”, Pattern Recognition Letters, pp.741–747, 1998.
[15] Navkirat Kaur, V. K. Banga, Avneet Kaur, “Image Segmentation Based on Color”, International Journal of Research in Engineering and Technology, Vol. 02(11), 2013.
[16] Frank Y. Shih, Shouxian Cheng, “Automatic seeded region growing for color image segmentation”, Image and Vision Computing, pp.877–886, 2005
[17] Khande Bharath Kumar and D. Venkataraman, “Object Detection Using Robust Image”, © Springer India 2015.
[18] Mihir Narayan Mohanty and Subhashree Rout, “An Intelligent Method for Moving Object Detection”, © Springer India 2015.
[19] Emmanuel Joy and J. Dinesh Peter, “Tracking of Unique Colored Objects: A Simple, Fast Visual Object Detection and Tracking Technique”, © Springer India 2015.
[20] Simranjit Singh Walia, Gagandeep Singh, “Color based Edge detection techniques– A review”, International Journal of Engineering and Innovative Technology, Vol. 3(9), 2014.
[21] Amiya Halder, Nilabha Chatterjee, Arindam Kar, Swastik Pal and Soumajit Pramanik, “Edge Detection: A Statistical approach”, 3rd International Conference on Electronics Computer Technology, 2011.
[22] Suryakant, Neetu Kushwaha, “Edge Detection using Fuzzy Logic in Matlab”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2(4), 2012.
[23] Shikha Bharti, Sanjeev Kumar, “An Edge Detection Algorithm based on Fuzzy Logic”, International Journal of Engineering Trends and Technology, Vol. 4(3), 2013.
[24] Mehul Thakkar, Prof. Hitesh Shah, “Edge Detection Techniques Using Fuzzy Thresholding”, IEEE, 2011.
[25] Anil K Jain, “Fundamentals of Digital Image Processing”, Pearson Education, ISBN 978-81-203-0929-6, 2014.
[26] E.Boopathi Kumar and V.Thiagarasu, “Evaluation of Color Image Segmentation Novel Methods”, IJIRCCE, Pp.21323 – 21328, Vol. 4(12), 2016.
[27] E.Boopathi Kumar and V.Thiagarasu, “Segmentation using Masking Methods in Color Images: an Approach”, IJESRT, Pp. 104-110, Vol. 6(2), 2017.
[28] A.Kalaivani, Dr.S.Chitrakala, “Automatic Color Image Segmentation”, International Conference on Science, Engineering and Management Research, IEEE, 2014.
[29] Md. Habibur Rahman, Md. Rafiqul Islam, “Segmentation of Color Image using Adaptive Thresholding and Masking with Watershed Algorithm”, IEEE, 2013.
[30] Rafael Guillermo Gonzalez, Junli Tao, “Generalization of Otsu’s Binarization into Recursive Color Image Segmentation”, IEEE, 2015.