An Efficient Approach for Edge Detection Technique Using Kalman Filter with Artificial Neural Network

Document Type : Original Article

Authors

1 Department of CSE, Rama University, Kanpur, India

2 Department of Computer and Information Sciences, Himalayan School of Science and Technology Swami Rama Himalayan University (SRHU) Jolly Grant Dehradun

Abstract

Edge identification is a technique for recognizing and detecting sharper breaks in an image. The halt is caused by a rapid change in the value of the pixel force dark level. Convoluting the picture with an administrator (Two-Directional channel) that is set to be noise sensitive is the standard approach for edge location. Edge finder is a method for locating precisely adjusted intensity esteem alterations that incorporate many significant neighborhoods image preparation methods. Edge recognition is a fundamental method in a wide range of image processing applications, including movement analysis, design identification, object recognition, clinical picture creation, and so on. It's recently shown up in a variety of edge detection systems, demonstrating both the advantages and disadvantages of these computations. The Kalman Filter with ANN method has two benefits that make it suitable for dealing with improvement issues: quicker merging and lower calculation rates. In this study, The ANN method was used to improve object localization accuracy. Kalman filtering is used to object coordinates acquired using the ANN method. Using ANN + Kalman Filtering increases localization accuracy and lowers localization error distances, according to the findings.

Keywords

References

  1. -S. Lu and C.-C. Chen, "Edge detection improvement by ant colony optimization", Pattern Recognition Letters, Vol. 29, No. 4, 416-425, 2008.
  2. Ashwini, Sapkal Mr. C. Bokhare and N. Z. Tarapore, Satellite Image Classification using the Back Propagation Algorithm of ANN, V. I. T. Pune, Reliance communication Mumbai.

 

 

 

 

 

  1. -Y. Chang, "Contextual-based Hopfield neural network for medical image edge detection," Optical Engineering, Vol. 45, (2006), doi: 037006-037006-9.
  2. Arbeláez, M. Maire, C. Fowlkes and J. Malik, "Contour detection and hierarchical image segmentation", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 5, (2011), 898-916.
  3. Rong, Z. Li, W. Zhang and L. Sun, "An improved Canny edge detection algorithm," IEEE International Conference on Mechatronics and Automation, (2014), 577-582, doi: 10.1109/ICMA.2014.6885761.
  4. Kanopoulos, N. Vasanthavada and R. L. Baker, "Design of an image edge detection filter using the Sobel operator," in IEEE Journal of Solid-State Circuits, Vol. 23, No. 2, 358-367, (1988), doi: 10.1109/4.996.
  5. Nguyen, J. Cho and S. B. Cho, "An architecture for real-time hardware co-simulation of edge detection in image processing using Prewitt edge operator," International Conference on Electronics, Information and Communications (ICEIC), (2014), 1-2, doi: 10.1109/ELINFOCOM.2014.6914387.
  6. Yang, K. I. Kou and C. Zou, "Edge detection methods based on modified differential phase congruency of monogenic signal", Multidimensional Systems and Signal Processing, Vol. 29, No. 1, (2016), 339-359.
  7. Alipoor, M., Imandoost, S, Haddadnia, J, “Designing Edge Detection Filters Using Particle Swarm Optimization”, proceeding of ICEE, 2010
  8. Ghosh, Chinmoy & Majumder, Suman & Ray, Sangram & Datta, Shrayasi & Nath, Satyendra & Ghosh, C. (2020). Different EDGE Detection Techniques: A Review. Doi: 10.1007/978-981-15-7031-5_84.
  9. Akhilesh Pandey, Shashi Kant Shukla “Classification of Devnagari Numerals using Multiple Classifier” International Journal of Computer Trends and Technology, 12, No. 4 (2014).
  10. Ding, Y. Xia and Y. Li, "Supervised segmentation of vasculature in retinal images using neural networks," International Conference on Orange Technologies, (2014), 49-52, doi: 10.1109/ICOT.2014.6954694.
  11. Quiza, R. and Davim, J.P., Computational methods and optimization. In Machining of Hard Materials, (2011), 177-208, Springer, London.
  12. Nikolic, E. Tuba and M. Tuba, "Edge detection in medical ultrasound images using adjusted Canny edge detection algorithm," 2016 24th Telecommunications Forum (TELFOR), (2016), 1-4, doi: 10.1109/TELFOR.2016.7818878.
  13. A. Günen, Ü. H. Atasever and E. Beşdok, "A novel edge detection approach based on backtracking search optimization algorithm
  14. (BSA) clustering", Proceedings of 8th International Conference on Information Technology (ICIT), (2017), 116-122.
  15. Ma, S. Liu, S. Hu, P. Geng, M. Liu and J. Zhao, "SAR image edge detection via sparse representation", Soft Compute, Vol. 22, No. 8, (2018), 2507-2515,
  16. Zhang, J.; Liu, Y.; Liu, H.; Wang, J. Learning Local–Global Multiple Correlation Filters for Robust Visual Tracking with Kalman Filter Redetection. Sensors 21, (2021), 1129.
International Journal of Engineering
Volume 34, Issue 12
TRANSACTIONS C: Aspects
December 2021
Pages 2604-2610

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