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A Comparative Analysis of Edge Detection Techniques for Processing of a Video Signal


Affiliations
1 Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, India
2 Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering, Visakhapatnam, India
3 Department of Computer Science and Engineering, Lovely Professional University, Punjab-11, India
 

In recent technology Edge detection technique is essential to image or video processing, which is The process of extract the structural information from digitized data. It involves various steps like object counting, feature extraction and classification. The goal of this study is to conduct a comparative examination of Edge Detection approaches used in video processing, with a greater emphasis on drastically reducing the dimensionality of image/video processing techniques. Edge detection techniques such as Sobel, Prewitt, Roberts, Laplacian, Canny, Krisch, and Robinson were compared. The evaluation of various edge detection approaches is based on characteristics such as PSNR, SNR, MSE, Entropy, and Execution time. Many video or image processing applications demand a quick processing response.



Keywords

Canny Edge,Image And Video Processing, Krisch, Laplacian,Prewitt, Roberts, Sobel Operators.
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  • R.C. Gonzalez and R.E. Woods, “Digital Image Processing”, Prentice Hall, Second Edition, 2002.

  • Zhonglan Wu and Pin Xu , “ Research on the technology of Video key- frame extraction based on clustering”, IEEE Fourth international conference on Multimedia Information networking and security, 2012, p.90-293.

  • T.Arunachalam, “An Efficient Color Image Segmentation using Edge Detection and Thresholding Methods, International Journal of Advanced Networking & Applications(IJANA) Volume: 08, Issue: 05 Pages: 19 – 23 (2017)

  • Fatemeh Soleimani Roozbahan, Reihaneh Azad “Security Solutions against Computer Networks Threats”, Int. J. Advanced Networking and Applications, Volume: 07 Issue: 01 Pages: 2576-2581 (2015) ISSN: 0975-0290

  • Mukhargee et al., “Key-frame estimation in video using randomness measure of feature point pattern”, IEEE transactions on circuits on systems for video technology, vol.7,no.5, May 2007, p. 612-620.

  • Zhao et al. “Key-frame extraction and shot retrieval using nearest feature line”, Proceedings of ACM Workshop on Multimedia, 2000,p. 217220.

  • Suresh C Raikwar, Charul Bhatnagar and Anand Singh Jalal, “A frame work for key-frame extraction from surveillance Video”, 5thInternational Conference on Computer and Communication Technology”, IEEE, 2014, p. 297-300.

  • Li Liu, Ling Shao, Peter Rocket, “Boosted key-frame and correlated pyramidal motion feature representation for human action recognition”, Pattern Recognition 45 (2013), p. 18101818.

  • Chi Chang-yanab, Zhang Ji-xiana, Liu Zheng-juna” Sudy on Methods of Noise Reduction In a Stripped Image”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B6b. Beijing 2008

  • Hameda Abd El-Fattah El-Sennary, Mohamed Eid Hussien, Abd El-Mgeid Amin Ali, Edge Detection of an Image Based on Extended Difference of Gaussian, American Journal of Computer Science and Technology. Vol. 2, No. 3, 2019, pp. 35-47. doi: 10.11648/j.ajcst.20190203.11

  • D.Maheswari, Dr.V.Radha, “Noise Removal In Compound Image Using Median Filter”, International Journal on Computer Science and Engineering, Vol. 02, No. 04, 2010, 1359-1362.

  • Mohsen Sharifi, Mahmoud Fathy and Maryam Tayefeh Mahmoudi, A Classified and Comparative Study of Edge Detection Algorithms, International Conference on Information Technology: Coding and Computing, IEEE, 2002.

  • Siwa Suwanmanee, Surapong Chatpun and Pedro Cabrales, Comparison of Video Image Edge Detection Operators on Red Blood Cells in Microvasculatur, IEEE, Biomedical Engineering International Conference, 2013.

  • Tamanna Sahoo, Sandipan Pine, “Design and Simulation Of Various Edge Detection Techniques Using Matlab Simulink”, IEEE, 2016.

  • G. N.Sarage, Dr. Sagar S Jambhorkar, “Noise Removal from Mammographic Image based on Mean and Median Filtering Techniques”, International Journal of Advanced Resea arch in Computer Science,Volume 2, No. 4, July-August 2011 498500.

  • Y. Li et al. Techniques for movie content analysis and skimming: tutorial and overview on video abstraction techniques, IEEE signal processing magazine 23(2)2006 p. 27-50.

  • Pinaki Pratim Acharjya, Ritaban Das and Dibyendu Ghoshal, ‘‘Study and Comparison of Different Edge Detectors for Image Segmentation’’, Global Journals Inc. (US), 2012.

  • Naveed Ejaz et al. , “ Adaptive key-frame extraction for video summarization using an aggregation mechanism”, Journal of Visual Communication 23 (2012), p.1031-140.

  • Shashidhar Ram Joshi, Roshankoju, ‘‘Study and Comparison of Edge Detection Algorithms”, IEEE, 2012.

  • Eric J.Wharton, Karen Panetta, S. Agaian, ‘‘Logarithmic Edge Detection with Applications’’, IEEE, 2007.

  • Dunamis et al. “A fuzzy video content representation for video summarization and content based retrieval”, Journal of signal processing, 2000, p.1049-1060.


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  • A Comparative Analysis of Edge Detection Techniques for Processing of a Video Signal

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Authors

Kesari Guru Vishnu
Department of Computer Science and Engineering, Maulana Azad National Institute of Technology, Bhopal, India
Kesari Eswar Bhageerath
Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering, Visakhapatnam, India
Asrith Vatsal Pallanti
Department of Computer Science and Engineering, Lovely Professional University, Punjab-11, India

Abstract


In recent technology Edge detection technique is essential to image or video processing, which is The process of extract the structural information from digitized data. It involves various steps like object counting, feature extraction and classification. The goal of this study is to conduct a comparative examination of Edge Detection approaches used in video processing, with a greater emphasis on drastically reducing the dimensionality of image/video processing techniques. Edge detection techniques such as Sobel, Prewitt, Roberts, Laplacian, Canny, Krisch, and Robinson were compared. The evaluation of various edge detection approaches is based on characteristics such as PSNR, SNR, MSE, Entropy, and Execution time. Many video or image processing applications demand a quick processing response.



Keywords


Canny Edge,Image And Video Processing, Krisch, Laplacian,Prewitt, Roberts, Sobel Operators.

References