Edge Detection Method Driven by Knowledge-Based Neighborhood Rules


  • Yavuz Çapkan Department of Mechatronics Engineering, Yildiz Technical University, Istanbul, Turkey
  • Halis Altun Department of Software Engineering, Istanbul Health and Technology University, Istanbul, Turkey
  • Can Bülent Fidan Department of Mechatronics Engineering, Karabuk University, Karabuk, Turkey




image processing, edge detection, computer vision, image analysis


Edge detection is a fundamental process, and therefore there are still demands to improve its efficiency and computational complexity. This study proposes a knowledge-based edge detection method to meet this requirement by introducing a set of knowledge-based rules. The methodology to derive the rules is based on the observed continuity properties and the neighborhood characteristics of the edge pixels, which are expressed as simple arithmetical operations to improve computational complexity. The results show that the method has an advantage over the gradient-based methods in terms of performance and computational load. It is appropriately four times faster than Canny method and shows superior performance compared to the gradient-based methods in general. Furthermore, the proposed method provides robustness to effectively identify edges at the corners. Due to its light computational requirement and inherent parallelization properties, the method would be also suitable for hardware implementation on field-programmable gate arrays (FPGA).


C. Deng, et al., “An Edge Detection Approach of Image Fusion Based on Improved Sobel Operator,” 4th International Congress on Image and Signal Processing, pp. 1189-1193, October 2011.

K. Zhang, et al., “An Improved Sobel Edge Algorithm and FPGA Implementation,” Procedia Computer Science, vol. 131, pp. 243-248, May 2018.

S. Taslimi, et al., “Adaptive Edge Detection Technique Implemented on FPGA,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 44, no. 4, pp. 1571-1582, March 2020.

L. Luo, et al., “A Vision Methodology for Harvesting Robot to Detect Cutting Points on Peduncles of Double Overlapping Grape Clusters in a Vineyard,” Computers in Industry, vol. 99, pp. 130-139, August 2018.

O. P. Verma, et al., “An Optimal Edge Detection Using Modified Artificial Bee Colony Algorithm,” Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, vol. 86, no. 2, pp. 157-168, March 2016.

V. Mario, et al., “Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence,” International Journal of Fuzzy Systems, vol. 23, no. 4, pp. 918-936, February 2021.

Y. Liu, et al., “Richer Convolutional Features for Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 8, pp. 1939-1946, August 2019.

N. S. Dagar, et al., “Edge Detection Technique Using Binary Particle Swarm Optimization,” Procedia Computer Science, vol. 167, pp. 1421-1436, April 2019.

C. Wen, et al., “Edge Detection with Feature Re-Extraction Deep Convolutional Neural Network,” Journal of Visual Communication and Image Representation, vol. 57, pp. 84-90, November 2018.

T. Peng-o, et al., “High Performance and Energy Efficient Sobel Edge Detection,” Microprocessors and Microsystems, vol. 87, Article no. 104368, November 2021.

Y. Çapkan, et al., “Robotic Arm Guided by Deep Neural Networks and New Knowledge-Based Edge Detector for Pick and Place Applications,” International Conference on Innovations in Intelligent Systems and Applications, pp. 1-4, August 2021.

Z. Chen, et al., “Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 103-116, July 2016.

Z. Chen, et al., “Vehicle Detection in High-Resolution Aerial Images Based on Fast Sparse Representation Classification and Multiorder Feature,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 8, pp. 2296-2309, July 2016.

H. Ma, et al., “Radar Image-Based Positioning for USV under GPS Denial Environment,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 1, pp. 72-80, January 2018.

D. Gupta, et al., “A Hybrid Edge-Based Segmentation Approach for Ultrasound Medical Images,” Biomedical Signal Processing and Control, vol. 31, pp. 116-126, January 2017.

W. C. Lin, et al., “Edge Detection in Medical Images with Quasi High-Pass Filter Based on Local Statistics,” Biomedical Signal Processing and Control, vol. 39, pp. 294-302, January 2018.

Z. Liu, et al., “Image Security Based on Iterative Random Phase Encoding in Expanded Fractional Fourier Transform Domains,” Optics and Lasers in Engineering, vol. 105, pp. 1-5, June 2018.

K. Hajipour, et al., “Edge Detection of Noisy Digital Image Using Optimization of Threshold and Self Organized Map Neural Network,” Multimedia Tools and Applications, vol. 80, no. 4, pp. 5067-5086, February 2021.

L. Xuan, et al., “An Improved Canny Edge Detection Algorithm,” 8th IEEE International Conference on Software Engineering and Service Science, pp. 275-278, November 2017.

K. Zhang, et al., “FPGA Implementation of Eight-Direction Sobel Edge Detection Algorithm Based on Adaptive Threshold,” Journal of Physics: Conference Series, vol. 1678, Article no. 012105, November 2020.

M. A. Günen, et al., “A Novel Edge Detection Approach Based on Backtracking Search Optimization Algorithm (BSA) Clustering,” 8th International Conference on Information Technology, pp. 116-122, May 2017.

J. Cao, et al., “Implementing a Parallel Image Edge Detection Algorithm Based on the Otsu-Canny Operator on The Hadoop Platform,” Computational Intelligence and Neuroscience, vol. 2018, Article no. 3598284, 2018.

M. Mittal, et al., “An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis,” IEEE Access, vol. 7, pp. 33240-33255, March 2019.

A. Al-Ghaili, et al., “Pixel Intensity-Based Contrast Algorithm (PICA) for Image Edges Extraction (IEE),” IEEE Access, vol. 8, pp. 119200-119220, June 2020.

Y. Tao, et al., “A Low Redundancy Wavelet Entropy Edge Detection Algorithm,” Journal of Imaging, vol. 7, no. 9, Article no. 188, September 2021.

P. Ganesan, et al., “Assessment of Satellite Image Segmentation in RGB and HSV Color Space Using Image Quality Measures,” International Conference on Advances in Electrical Engineering, pp. 1-5, January 2014.

Z. Wang, et al., “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004.

“Alphabetrecognizer Dataset,” https://github.com/MinhasKamal/AlphabetRecognizer, June 01, 2021.

“Image Dataset,” https://github.com/yavuzcpkn/Knowledge-Based-Edge-Detection, January 01, 2022.

A. P. Kelm, et al., “Object Contour and Edge Detection with RefineContourNet,” International Conference on Computer Analysis of Images and Patterns, pp. 246-258, August 2019.




How to Cite

Y. Çapkan, H. Altun, and C. B. Fidan, “Edge Detection Method Driven by Knowledge-Based Neighborhood Rules”, Int. j. eng. technol. innov., vol. 13, no. 1, pp. 01–13, Jan. 2023.