Subscribe Now Subscribe Today
Research Article

Enhanced Boundary Detection Method Based on Canny Theory

J. Juan Zhao, Jin Wang, Wei Wei, Xiao Min Chang and Bo Pei
Facebook Twitter Digg Reddit Linkedin StumbleUpon E-mail

With the quick development of computer image detection technique, image boundary inspect method has become the field of image processing and computer vision research focus, digital image boundary inspection is image segmentation, image recognition, image analysis for instance area shape extracting the important foundation. Based on Canny operator manually select the threshold improperly, which result in edge detection has some ineffective shortcomings. In this study, the method is presented an reinforced Canny boundary inspection to automatically produc an adaptive threshold for Canny edge detection operator. In the algorithm, it can create the threshold parameter automatically, by the mean square error and average gray of the image. Therefore this method can avoid errors caused by manual input and obtain a desired edge effect.

Related Articles in ASCI
Search in Google Scholar
View Citation
Report Citation

  How to cite this article:

J. Juan Zhao, Jin Wang, Wei Wei, Xiao Min Chang and Bo Pei, 2013. Enhanced Boundary Detection Method Based on Canny Theory. Information Technology Journal, 12: 6723-6728.

DOI: 10.3923/itj.2013.6723.6728



1:  Canny, J., 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., 8: 679-698.
CrossRef  |  Direct Link  |  

2:  Demigny, D. and T. Kamle, 1997. A discrete expression of Canny's criteria for step edge detector performances evaluation. IEEE Trans. Pattern Anal. Mach. Intell., 19: 1199-1211.
CrossRef  |  

3:  Han, W.Y. and J.C. Lin, 1997. Minimum-maximum exclusive mean (MMEM) filter to remove impulse noise from highly corrupted images. Electronics Lett., 33: 124-125.
CrossRef  |  

4:  Hanmandlu, M., A.K. Tiwari, V.K. Madasu and S. Vasikarla, 2006. Mixed noise correction in gray images using fuzzy filters. Proceedings of the 3rd International Conference on Information Technology New Generations, April 10-12, 2006, Las Vegas, NV., pp: 547-553
CrossRef  |  

5:  Liqin, S., S. Dinggang and Q. Feihu, 1994. Edge detection on real time using LOG filter. Proceedings of the International Symposium on Speech, Image Processing and Neural Networks, April 13-16, 1994, China, pp: 37-40
CrossRef  |  

6:  Li, R. and Y.J. Zhang, 2003. A hybrid filter for the cancellation of mixed Gaussian noise and impulse noise. Proceedings of the Joint Conference of the 4th International Conference on Information, Communications and Signal Processing and 4th Pacific Rim Conference on Multimedia, Volume 1, December 15-18, 2003, China, pp: 508-512
CrossRef  |  

7:  Masoud, A.A. and M.M. Bayoumi, 1995. Using local structure for the reliable removal of noise from the output of the LoG edge detector. IEEE Trans. Syst. Man Cybernetics, 25: 328-337.
CrossRef  |  

8:  Nallaperumal, K., J. Varghese, S. Saudia, K. Arulmozhi, K. Velu and S. Annam, 2007. Salt and pepper impulse noise removal using adaptive switching median filter. Proceedings of the Asia Pacific OCEANS, May 16-19, 2007, Singapore, pp: 1-8
CrossRef  |  

9:  Ng, P.E. and K.K. Ma, 2006. A switching median filter with boundary discriminative noise detection for extremely corrupted images. IEEE Trans. Image Process., 15: 1506-1516.
CrossRef  |  

10:  Pal, S.K. and R.A. King, 1983. On edge detection of X-ray images using fuzzy sets. IEEE Trans. Pattern Anal. Mach. Intell., 5: 69-77.
CrossRef  |  

11:  Prewitt, J.M.S., 1970. Object Enhancement and Extraction. Academic Press, New York

12:  Wang, Y., D. Liang and H. Ma, 2006. An algorithm for image denoising based on mixed filter. Proceedings of the 6th World Congress on Intelligent Control and Automation, Volume 2, June 21-23, 2006, Dalian, pp: 9690-9693
CrossRef  |  

©  2022 Science Alert. All Rights Reserved