Design of Improved Distributed Canny Edge Detection Algorithm (IDCEDA) and its VLSI Implementations

Authors(3) :-Ch. Janardhan, Dr. K. V. Ramanaiah, Dr. K. Babulu

Recently Automatic Image Segmentation and edge detection techniques have become more popular and commonly used in many applications like Road Sign Detection in ADAS systems, Medical Image Diagnosis Machine vision systems etc. Generally, information about the object is available at the edges or boundaries and high frequency noise or an artifact exists in the boundaries due to improper image acquisition process. Hence, it is very difficult to interpret or process such type of images. In this paper we proposed improved distributed canny edge detection algorithm (IDCEDA) to segment or detection of the object boundaries into more accurate and it is synthesized ISE environment the final layout is developed through TSMC 0.18um technology. The proposed design gives more accurate results with minimum no. of hardware resources compared to existing approaches in terms of accuracy and less hardware resources required for implantation. The proposed algorithm performs superior than the existing approaches in terms of Hardware Resources Utilized and sharp edge boundaries of images. Finally, the algorithm is implemented on vertex family of FPGA devices for effective estimation of Real time performance of the proposed algorithm.

Authors and Affiliations

Ch. Janardhan
Department of Electronics & Communication, JNT University Kakinada, Kakinada, Andhrapradesh, India
Dr. K. V. Ramanaiah
Department of Electronics & Communication, Yogivemana University, Proddatur, Andhrapradesh, India
Dr. K. Babulu
Department of Electronics & Communication, JNT University Kakinada, Kakinada, Andhrapradesh, India

Edge Detection, Image Segmentation, De-noise, FPGA, VLSI Architecture

  1. Ruzon, Mark A., and Carlo Tomasi. "Color edge detection with the compass operator."Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference On.. Vol. 2. IEEE, 1999.
  2. Fathy, Mahmood, and Mohammed Yakoob Siyal. "An image detection technique based on morphological edge detection and background differencing for real-time traffic analysis."Pattern Recognition Letters 16.12 (1995): 1321-1330.
  3. Gonzalez, Rafael C., and Richard E. Woods. "Digital image processing."(1992).
  4. Shih, Ming-Yu, and Din-Chang Tseng. "A wavelet-based multiresolution edge detection and tracking."Image and Vision Computing 23.4 (2005): 441-451.
  5. Tizhoosh, Hamid R. "Fast fuzzy edge detection."Fuzzy Information Processing Society, 2002. Proceedings. NAFIPS. 2002 Annual Meeting of the North American. IEEE, 2002.
  6. Pal, Chandrajit, et al. "Design space exploration for image processing architectures on FPGA targets."arXiv preprint arXiv:1404.3877 (2014).
  7. 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.
  8. Yu-qian, Zhao, et al. "Medical images edge detection based on mathematical morphology."Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the. IEEE, 2006.
  9. Xu, Qian, Chaitali Chakrabarti, and Lina J. Karam. "A distributed Canny edge detector and its implementation on FPGA."Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE), 2011 IEEE. IEEE, 2011.
  10. Kanopoulos, Nick, Nagesh Vasanthavada, and Robert L. Baker. "Design of an image edge detection filter using the Sobel operator."IEEE Journal of solid-state circuits 23.2 (1988): 358-367.
  11. Chen, Sao-Jie, et al. Hardware software co-design of a multimedia SOC platform. Springer Science & Business Media, 2009.
  12. Abbasi, T. A., and M. U. Abbasi. "A novel FPGA-based architecture for Sobel edge detection operator."International Journal of Electronics 94.9 (2007): 889-896.
  13. Zhang, Wei Tang, and Shao Gang Huang. "Adaptive Neural Network for Image Edge Detection."Advanced Materials Research. Vol. 524. Trans Tech Publications, 2012.
  14. Sultana, Azeema, and M. Meenakshi. "Design and development of fpga based adaptive thresholder for image processing applications."Recent Advances in Intelligent Computational Systems (RAICS), 2011 IEEE. IEEE, 2011.
  15. Deepa, P., and C. Vasanthanayaki. "VLSI Implementation of Enhanced Edge Preserving Impulse Noise Removal Technique."VLSI Design and 2013 12th International Conference on Embedded Systems (VLSID), 2013 26th International Conference on. IEEE, 2013.
  16. Chen, Haiguang, et al. "A fast filtering algorithm for image enhancement."IEEE transactions on medical imaging 13.3 (1994): 557-564.
  17. Li, Hui-Ya, Wen-Jyi Hwang, and Chia-Yen Chang. "Efficient fuzzy C-means architecture for image segmentation."Sensors11.7 (2011): 6697-6718.
  18. C. Janardhan, K. V Ramanaiah, and K. Babulu, "A Novel Approach for Solving Medical Image Segmentation Problems with ACM,” Int. Journal of Engineering Research and Application, Vol. 7, Issue 11, November 2017, pp.40-47.
  19. Mishra and M. Agarwal, "Hardware and Software Performance of Image Processing Applications on Reconfigurable Systems,” 2015 Annu. IEEE India Conf. (INDICON), New Delhi, pp. 1–5, 2015.
  20. Draper, Bruce A., et al. "Accelerated image processing on FPGAs."IEEE transactions on image processing 12.12 (2003): 1543-1551.

Publication Details

Published in : Volume 3 | Issue 1 | January-February 2018
Date of Publication : 2018-02-28
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 77-84
Manuscript Number : CSEIT183115
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Ch. Janardhan, Dr. K. V. Ramanaiah, Dr. K. Babulu, "Design of Improved Distributed Canny Edge Detection Algorithm (IDCEDA) and its VLSI Implementations ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.77-84, January-February-2018.
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