Edge detection based on ant colony optimization using adaptive thresholding technique

Автор: Pragya Gautam, Krishna Raj

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 7 vol.10, 2018 года.

Бесплатный доступ

Image edge detection is a process where true edges of an image are identified. In past, gradient based methods in which first or second order pixel difference is used to find discontinuities and if magnitude value of gradient is higher than certain threshold then that pixel under observation is identified as edge pixel. These methods are full of error, because in addition to true edges they also find false edges and infect false edges are more in comparison to true edges. To solve such problem, swarm intelligence based ant colony optimization based edge detection method is detailed where numbers of falsely detected edges are very small. The performance of the ant colony optimization (ACO) is done in terms of Peak Signal to Noise Ratio, Performance Ratio and Efficiency.

Еще

Ant Colony Optimization (ACO), Edge Detection, Peak Signal to Noise Ratio (PSNR), Performance Ratio (PR), Efficiency (EF)

Короткий адрес: https://sciup.org/15015981

IDR: 15015981   |   DOI: 10.5815/ijigsp.2018.07.07

Список литературы Edge detection based on ant colony optimization using adaptive thresholding technique

  • R. Gonzalez and R. Woods, “Digital Image Processing,” Addison Wesley, 1992, pp 414 - 428.
  • Maini, Raman, and Himanshu Aggarwal. "Study and comparison of various image edge detection techniques." International journal of image processing (IJIP) 3.1 (2009): 1-11.
  • Jaiswal, Utkarsh, and Shweta Aggarwal. "Ant colony optimization." International Journal of Scientific and Engineering Research 2.7 (2011): 1-7.
  • Vasavada J, Tiwari S. “A Hybrid Method for Detection of Edges in Grayscale Images.” International Journal of Image, Graphics and Signal Processing. 2013 Jul 1; 5(9):21.
  • Mamoria P, Raj D. An Analysis of Fuzzy and Spatial Methods for Edge Detection. International Journal of Information Engineering & Electronic Business. 2016 Nov 1; 8(6).
  • Xin G., Ke C., and Xiaoguag H. (2012). An improved Canny edge detection algorithm for color image. Institute of Electrical and Electronics Engineers Transcations, pp.113-117.
  • Wang, Rong, et al. "An edge detection method by combining fuzzy logic and neural network." Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on. Vol. 7. IEEE, 2005.
  • Lyvers, Edward P., et al. "Subpixel measurements using a moment-based edge operator." IEEE Transactions on pattern analysis and machine intelligence 11.12 (1989): 1293-1309.
  • Gupta S., & Mazumdar S. G. (2013). Sobel edge detection algorithm. International journal of computer science and management Research, 2(2), pp.1578-1583.
  • Katiyar, & Arun. (2014). Comparative analysis of common edge detection techniques in context of object extraction. IEEE Transactions of Geoscience and Remote Sensing, 50(11), pp.68-79.
  • Jena, Kalyan Kumar. "Application of COM-SOBEL operator for edge detection of images." IJISET, Engineering & Technology 2.4 (2015): 48-51.
  • J.-J. Hwang and T.-L. Liu. Pixel-wise deep learning for contour detection. In ICLR, 2015.
  • Om Prakash Verma et. al., “A Novel Fuzzy Ant System for Edge Detection”, in Proc. of the 9th IEEE International Conference on Computer and Information Science, pp.228-233, 2010.
  • Stutzle and H. Holger H, “Max-Min ant system,” Future Generation Computer Systems , vol. 16, pp. 889–914, Jun. 2000
  • Jin-Yu, Zhang, Chen Yan, and Huang Xian-Xiang. "Edge detection of images based on improved Sobel operator and genetic algorithms." Image Analysis and Signal Processing, 2009. IASP 2009. International Conference on. IEEE, 2009.
  • Srinivasan, V., P. Bhatia, and Sim Heng Ong. "Edge detection using a neural network." Pattern Recognition 27.12 (1994): 1653-1662.
  • M. Dorigo, L. M. Gambardella, M. Middendorf and T. Stutzle, “Special Issue on Ant Colony Optimization”, IEEE Transactions on Evolutionary Computation, vol. 6, Jul. 2002.
  • Gupta S., and Mazumdar S. G. (2013). Sobel edge detection algorithm. International journal of computer science and management Research, 2(2), pp.1578-1583.
  • D.S. Lu and C.C. Chen, “Edge detection improvement by ant colony optimization”, Pattern Recognition Letters, vol. 29, pp. 416–425, Mar.2008.
  • Jing Tian, Weiyu Yu, and Shengli Xie, ”An Ant Colony Optimization Algorithm for Image Edge Detection”, in Proc. of the IEEE International, pp.751-756, 2008.
  • Gupta, Charu, and Sunanda Gupta. "Edge detection of an image based on ant colony optimization technique." Int. J. Sci. Res.(IJSR) 2.6 (2013): 1256-1260.
  • X. Zhuang, “Edge Feature Extraction in Digital Images with the Ant Colony System”, in proc. of the IEEE international Conference an computational intelligence for Measurement Systems and Applications, pp.133-136, 2004.
  • R. Rajeswari and R. Rajesh, “A modified ant colony optimization based approach for image edge detection,” International Conference on Image Information Processing (ICIIP), pp. 1–6, 2011.
  • M. Dorigo and S. Thomas, “Ant Colony Optimization”. Cambridge: MIT Press, 2004.
  • H.B. Duan, “Ant Colony Algorithms: Theory and Applications”. Beijing: Science Press, 2005.
  • M. Dorigo, M. Birattari and T. Stutzle, “Ant colony optimization”, in proc. of the IEEE Computational Intelligence Magazine, pp.28.39, 2006.
  • Anna Veronica Baterina and Carlos Oppus, “Image Edge Detection Using Ant Colony Optimization”, International Journal of circuits, System and Signal Processing, Issue 2 vol.4, pp. 25-33, 2010.
  • J. Tian, W. Yu, and S. Xie, “An ant colony optimization algorithm for image edge detection,” in IEEE World Congress on Evolutionary Computation, pp. 751 –756, Jun. 2008.
  • P. Xiao, J. Li, and J.-P. Li, “An improved ant colony optimization algorithm for image extracting,” in Apperceiving Computing and Intelligence Analysis (ICACIA), 2010 International Conference on, pp. 248 –252, Dec. 2010.
  • N. Otsu, “A Threshold Selection Method from Gray-level Histograms,” IEEE Trans. on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
  • Khaire, Pushpajit A., and Nileshsingh V. Thakur. "A fuzzy set approach for edge detection." International Journal of Image Processing (IJIP) 6.6 (2012): 403-412.
Еще
Статья научная