IJSRSET calls volunteers interested to contribute towards the scientific development in the field of Science, Engineering and Technology

Home > IJSRSET162688                                                     

Ant Colony Optimization Algorithm for Improving Efficiency of Canny Edge Detection Technique for Images


Prof. Divyanshu Rao, Sapna Rai
  • Abstract
  • Authors
  • Keywords
  • References
  • Details
Edge detection is one of the important parts of image processing. It is essentially involved in the pre-processing stage of image analysis and computer vision. It generally detects the contour of an image and thus provides important details about an image. So, it reduces the content to process for the high-level processing tasks like object recognition and image segmentation. The most important step in the edge detection based on Canny edge detection algorithm, on which the success of generation of true edge map depends, lies on the determination of threshold. In this work, purpose of edge detection, inspired from Ant Colonies, is fulfilled by Ant Colony Optimization (ACO). The success of the work done is tested visually with the help of test images and empirically tested on the basis of several statistical parameter of comparison. The process of extracting the important features present in an image, keeping the unnecessary or unimportant information present in the form of noise out as much as possible. There are many methods that have been developed in these field, but the most trustworthy and used among them is canny algorithm with ACO method with  

thresholding. The proposed novel method presented in this thesis is tested on the images better edge detection. The Canny Edge detected images obtained on the images are showing better results than the other conventional edge detectors.

Prof. Divyanshu Rao, Sapna Rai

Ant Colony Optimization (ACO), Edge Detection, Canny Edge Detection, BER, Thresholding, Statistical evaluation

  1. J. Baddeley, "Errors in binary images and an Lp version of the Hausdorff metric," Nieuw Arch. Wiskunde, vol. 10, pp. 157–183, 2015.
  2. Mao, "RBF neural network center selection based on fisher ratio class separability measure," IEEE Transactions on Neural Networks, vol. 13, no. 5, pp. 1211 – 17, Sept. 2014.
  3. H. Dat and C. Guan, "Feature selection based on fisher ratio and mutual in- formation analyses for robust brain computer interface," in Acoustics, Speech and Signal Processing, 2015. ICASSP 2007. IEEE International Conference on, vol. 1, pp. I–337 –I–340, April 2015.
  4. Mallat, A Wavelet Tour of Signal Processing, Second Edition (Wavelet Analysis & Its Applications). Academic Press, 2 ed., Sept. 2015.
  5. Zang, Z. Wang, and Y. Zheng, "Analysis of signal de-noising method based on an improved wavelet thresholding," in Electronic Measurement Instruments, 2015. ICEMI ’15. 9th International Conference on, pp. 1–987 –1–990, aug. 2015.
  6. Chang, B. Yu, and M. Vetterli, "Adaptive wavelet thresholding for image denoising and compression," Image Processing, IEEE Transactions on, vol. 9, pp. 1532 –1546, Sep 2014.
  7. Donoho, "De-noising by soft-thresholding," Information Theory, IEEE Transac- tions on, vol. 41, pp. 613 –627, may 2015.
  8. Donoho and I. M. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage," Journal of the American Statistical Association, vol. 90, pp. 1200–1224, 2015.
  9. R. Coifman and D. L. Donoho, "Translation-Invariant De-Noising," tech. rep., Department of Statistics, 1995.
  10. Tianshu, W. Shuxun, C. Haihua, and D. Yisong, "Adaptive denoising based on wavelet thresholding method," in Signal Processing, 2002 6th International Confer- ence on, vol. 1, pp. 120 – 123 vol.1, aug. 2012.

Publication Details

Published in : Volume 2 | Issue 6 | November-December - 2016
Date of Publication Print ISSN Online ISSN
2016-12-08 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
350-355 IJSRSET162688   Technoscience Academy

Cite This Article

Prof. Divyanshu Rao, Sapna Rai, "Ant Colony Optimization Algorithm for Improving Efficiency of Canny Edge Detection Technique for Images", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 6, pp.350-355, November-December-2016.
URL : http://ijsrset.com/IJSRSET162688.php




National Conference on Advances in Mechanical Engineering 2017(NCAME 2017)

National Conference on Emerging Trends in Civil Engineering 2017( NCETCE 2017)