International Journal of
Computer Sciences and Engineering

Scholarly Peer-Reviewed, and Fully Refereed Scientific Research Journal
Spatial Domain Edge Detection of Image in Rainy Weather
Spatial Domain Edge Detection of Image in Rainy Weather
Veena Dohare1 , M.P. Parsai2
1 Dept. of Electronics and Telecommunication, Jabalpur Engineering College, Jabalpur, India.
2 Dept. of Electronics and Telecommunication, Jabalpur Engineering College, Jabalpur, India. .
Correspondence should be addressed to: veena0293@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-7 , Page no. 32-38, Jul-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i7.3238

Online published on Jul 30, 2017

Copyright © Veena Dohare, M.P. Parsai . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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Citation

IEEE Style Citation: Veena Dohare, M.P. Parsai, “Spatial Domain Edge Detection of Image in Rainy Weather”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.7, pp.32-38, 2017.

MLA Style Citation: Veena Dohare, M.P. Parsai "Spatial Domain Edge Detection of Image in Rainy Weather." International Journal of Computer Sciences and Engineering 5.7 (2017): 32-38.

APA Style Citation: Veena Dohare, M.P. Parsai, (2017). Spatial Domain Edge Detection of Image in Rainy Weather. International Journal of Computer Sciences and Engineering, 5(7), 32-38.
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Abstract :
Edges are the set of curved line segments where brightness level of image changes sharply. It is one of the most important information of an image which can helps to detect object boundary, its relative position within target area and many other useful information. In edge detection process, edges are retrieved from an image by spotting high intensity variations of the pixels. Edge detection of an image minimizes the amount of processed data effectively and discards information that is less important, keeping the important structural properties of an image. This paper presents a different approach to apply Gradient and LoG operator to get more continuous edges than the conventional one using MATLAB. Their results are compared using peak signal to noise ratio (PSNR). Two images in rainy weather are taken by my camera for case study. It can be used in many applications such as in object tracking, in data compression, in image analysis and medical imaging.
Key-Words / Index Term :
Gradient and LoG; Peak signal-to-noise ratio; Intensity level; Edge detection
References :
[1] G.T.Shrivakshan & Dr. C. Chandrasekhar, “A comparison of various edge detection techniques used in image processing” IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No. 1, pp. 269-276, September 2012.
[2] Mohamed A. El-Sayed, “A new algorithm based Entropic threshold for edge detection in images” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No. 1, pp. 71-78, September 2011.
[3] Kumar & Sukhwinder Singh, “Edge detection and denoising medical images using Morphology” International Journal of Engineering Sciences & Emerging Technologies, Vol. 2, Issue 2, pp. 66-72, June 2012.
[4] Rashmi Dubey, Rajendra Prasad Singh, Dr. Sarika Jain & Dr. Rakesh Singh Jadon “Quantum methodology for edge detection: A compelling approach to enhance edge detection in Digital image processing” In the proceeding of the 2014 5th International Conference- Confluence The Next Generation Information Technology Summit, pp. 631-636, 2014.
[5] Sasmita Mishra, “Edge detection of images: A novel approach” International Journal of Advanced Research in Computer Science and Software Engineering 5(3), Vol. 5, Issue 3, pp. 1213-1215, March 2015.
[6] Swetha.M & Jyoshna.C, “Boundary detection in medical images using edge field vector based on Law’s texture and Canny method” International Journal of Engineering Trends and Technology (IJETT) Vol. 4, Issue 5, pp. 1912-1917, May 2013.
[7] Ballado, A.H.Jr., Dela Cruz, J.C., Avendaño, G. O., Echano, N. M., Ella, J. E., Medina, M.E.M., Paquiz,B.K.C.“Philippine currency paper bill counterfeit detection through image processing using Canny edge technology” 8th IEEE International Conference Humanoid, Nanotechnology, Information Technology Communication and Control, Environment and Management (HNICEM) The Institute of Electrical and Electronics Engineers Inc. (IEEE) – Philippine Section, Philippines, December 2015.
[8] Bo Lia, Aleksandar Jevticb, Ulrik Söderströma, Shafiq Ur Réhmana, Haibo Li “Fast edge detection by center of mass” Proceedings of the 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing pp. 103-110, Japan, 2013.
[9] Y. Ramadevi, T. Sridevi, B. Poornima, B. Kalyani “Segmentation and object recognition using edge detection techniques” International Journal of Computer Science & Information Technology (IJCSIT), Vol. 2, No. 6, pp. 153-161, December 2010.
[10] Sunanda Gupta, Charu Gupta & S.K.Chakarvarty “Image edge detection: A review” International journal of advance research in computer science and technology (IJARCET) Vol. 2, Issue 7, pp. 2246-2251, July 2013.
[11] Veena Dohare, Prof. M.P. Parsai, “A review of speed performance evaluation of various edge detection methods of images”, Indian journal of computer science and engineering, Vol.8, No. 2, pp: 128-138, Apr-May 2017.
[12] K. Rajalakshmi, K. Nirmala, “Heart disease analysis using support vector machine and Sobel edge detection” International Journal of Computer sciences and engineering, Vol. 5, Issue 4, pp. 5-13, Apr 2017.