A Review on Edge Detection Technique in Image Processing Techniques

Authors

  • Prof. Divyanshu Rao  Shri Ram Institute of Technology, Jabalpur, Madhya Pradesh, India
  • Sapna Rai  Shri Ram Institute of Technology, Jabalpur, Madhya Pradesh, India

Keywords:

Edge detectors, Image Processing, Pattern recognition, Object Recognition.

Abstract

Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity scene. Traditional method of edge detection involves convolving the image with an operator (2- D filter) which is constructed to be sensitive to large gradients. Edge detectors form a collection of very important local image processing method to locate sharp changes in the intensity function. Edge detection is an important technique in many image processing applications such as object recognition, motion analysis, pattern recognition, medical image processing etc. This paper shows the comparison of edge detection techniques under different conditions showing advantages and disadvantages of the selected algorithms. This was done under Matlab. Further work would be to develop a novel algorithm using the working on the disadvantages and advantages of the existing one to create a novel edge detector.

References

  1. Ibrahiem, M.EL Emery, On the application of Artificial Neural in analysing and classifying human chromosome, Journal of Computer science vol. 2(1) pp. 72-75 2015.
  2. Senthilkumaran and R.Rajesh, A study on edge detection methods for image segmentation, Proceedings of the international Conference on Mathematics and Computer Science (ICMCS-2009) vol. 1, pp. 255-259, 2014.
  3. Rosenfel, Computer vision, a source of models for biological visual process, IEEE Transaction on Biomedical 36(1), pp. 83-94, 2013.
  4. Sobel, Neighbourhood coding of binary images fast contour following and general array binary processing, Computer graphics and image processing vol. 8, pp. 127- 135, 2012.
  5. Marr, E.C.Hildreth. Theory of edge detection, proceeding of the Royal Society, 201b, pp187-217, 2014.
  6. Canny. A computational approach to edge detection, IEEE Transactions in pattern analysis and machine intelligence vol. 8 pp. 679-698, 2013.
  7. H. Hueckel, An operator which locate edges in digitized pictures, Journal of ACM vol. 18, pp. 113-125, 2015.
  8. Maini, H.Aggarwal. Study and comparison of various image edge detection techniques, International Journal of Image processing (IJIP), volume (3), issue (1) 2016.
  9. Senthilkumaran and R.Rajesh, Edge Detection Techniques for image segmentation-A survey, Proceedings of the international conference on managing next generation software applications (MNGSA-08) pp. 749-760, 2013.
  10. 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, 2015.
  11. Senthilkumaran and R.Rajesh, A study on split and merge for region based image segmentation, proceedings of UGC sponsored national conference network security (NCNS-08) pp57-61, 2014.
  12. BIN Wen, H.Zheng and Z. Tao. Multiscale Unsupervised Segmentation of SAR Imagery using the generic algorithm, Sensors, vol8, pp. 1704-1711, 2012.
  13. Paulinas and A.Usnskas, A survey of generic algorithm application for image enhancement and segmentation, information technology and control vol. 36, no 3, pp. 278-284, 2013.
  14. C. Gonzalez and R.E.Woods, Digital Image Processing 2nd ed. Prentice Hall, 2002.
  15. A.Kirsch, Computer determination of the constituent structure of biomedical images, comput Eiorned.Res. Vol. 4, pp315-325 12014.
  16. Yakimovsky. Boundary and object detection in real world images. Journal of ACM, vol. 23, no 4 pp. 598-619, 2014.
  17. Yuille and T.A.Poggio. Scaling theorems for zero crossing. IEEE Transacation on Pattern Anal.Machine Intelligence vol. 8, no.1 pp. 157-163, 2015.
  18. Heath, S. Sarkar,T.Sanocki and K.W.Bowyer. A Robust visual method for assessing the relative performance of edge detection algorithm. IEEE Trans. Pattern Analysis and machine intelligence vol. 12 pp. 1338-1359, 2012.
  19. Heath,S.Sarkar,T.Sanocki and K.W.Bowyer. Comparison of edge detector. A methodology and initial study, computer vision and image understanding vol. 69, no 1.pp38-54, 2011.
  20. C. Shin, D.Goldgof and K.W.Bowyer. Comparison of edge detector performance through use in an object recognition task, computer vision and image understanding vol. 84, no1,pp 160-178, 2012.
  21. Peli and D.malah. A study of edge detection algorithm, computer graphics and image processing vol20 pp. 1-21, 2011].

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2016-12-08

Issue

Volume 2, Issue 6, November-December-2016

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. Divyanshu Rao, Sapna Rai "A Review on Edge Detection Technique in Image Processing Techniques" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 6, pp.345-349, November-December-2016.