CFP last date
20 December 2024
Reseach Article

A Comparative Analysis of Thresholding and Edge Detection Segmentation Techniques

by Jaskirat Kaur, Sunil Agrawal, Renu Vig
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 15
Year of Publication: 2012
Authors: Jaskirat Kaur, Sunil Agrawal, Renu Vig
10.5120/4898-7432

Jaskirat Kaur, Sunil Agrawal, Renu Vig . A Comparative Analysis of Thresholding and Edge Detection Segmentation Techniques. International Journal of Computer Applications. 39, 15 ( February 2012), 29-34. DOI=10.5120/4898-7432

@article{ 10.5120/4898-7432,
author = { Jaskirat Kaur, Sunil Agrawal, Renu Vig },
title = { A Comparative Analysis of Thresholding and Edge Detection Segmentation Techniques },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 15 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number15/4898-7432/ },
doi = { 10.5120/4898-7432 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:32.778339+05:30
%A Jaskirat Kaur
%A Sunil Agrawal
%A Renu Vig
%T A Comparative Analysis of Thresholding and Edge Detection Segmentation Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 15
%P 29-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Thresholding and edge detection being one of the important aspects of image segmentation comes prior to feature extraction and image recognition system for analyzing images. It helps in extracting the basic shape of an image, overlooking the minute unnecessary details. In this paper using image segmentation (thresholding and edge detection) techniques different geo satellite images, medical images and architectural images are analyzed. To quantify the consistency of our results error measure is used.

References
  1. Alina Doringa, Gabriel Mihai, Dan Burdescu, “comparison of two image segmentation algorithms” Second international conference on advances in multimedia, IEEE computer society, pp. 185-190 (2010)
  2. Mr Salem Saleh Al-amri, Dr. N.V. Kalyankar, Dr.Khamitkar S.D “Image segmentation by using edge detection”, International journal on computer science and engineering, vol. 02, No. 03, 2010, pp. 804-807
  3. N.Senthilkumaram, R.Rajesh “Edge detection techniques for image segmentation-A survey of soft computing approach”, International journal of recent trends in engineering , vol.1, No.2, May 2009,pp. 250-254
  4. Steven L Eddins, Richard E.woods, Rafael C “digital image processing” (2009)
  5. Huang, Yourui; Wang, Shuang “Multilevel thresholding Methods for image segmentation with otsu based on QPSO”, Image and signal processing, CISP 2008, vol. 3,pp.701-705
  6. Shiping Zhu, Xi Xia, Qingrong Zhang, kamel Belloulata “An image segmentation in image processing based on threshold segmentation”, Third international IEEE conference on signal-Image Technologies and internet- based system, SITIS 2007 ,pp. 673-678
  7. Kalelia, F.; Aydina, N.; Ertas, G.;Gulcur, H.O.; “An adaptive approach to the segmentation of DCE-MR images of the breast: comparison with classical thresholding algorithms” ,Computational intelligence on image and signal processing. CIISP 2007,IEEE symposium, pp. 375-379
  8. Raman Maini, Dr.Himanshu Aggarwal “Study and comparison of various edge detection techniques”, International journal of image processing, vol. 3: issue(1)
  9. Hong Shan Neoh, Asher Hazan chuk “Adaptive edge detection for real-time video processing using FPGAs”, 101 innovation drive, San jose, CA
  10. Spreeuwers, L.J.; Van der Heijden , F.; “Evaluation of edge detection using average risk”, Pattern recognition , vol. 3 ,Conference on Image ,speech and signal analysis, pp. 771-774, (2002)
  11. Google Images. Retrieved on November 5, 2011 from http://www.google.ca/imghp?hl=en&tab=wi
Index Terms

Computer Science
Information Sciences

Keywords

e_rms Error measures algorithms