A COMPREHENSIVE REVIEW ON SOBEL EDGE DETECTOR UTILIZING GRAY SCALE IMAGES
DOI:
https://doi.org/10.65009/6rf2v215Keywords:
service-oriented, architecture design, energy consumption, enterprise service bus Quality of Experience (QoE), Quality of Protection (QoP).,,Abstract
In digital image processing, an edge is a change in a digital image's sharpness. The border of
a greyscale image, which may be black or white, may undergo these changes. The key function of any
edge detector is to extract crucial information from the image that is hidden and impossible to obtain with
simple methods. To find edges, the range non-continuity principle is applied. Although there are many
different edge detection operators, we'll concentrate on the Sobel Edge Detector because it's one of the
most often used. Researchers can use the information in this document to learn the fundamentals of the
Sobel Edge Detector, which was created to support the development of beginning edge detection
research. Edge detection is frequently used in many different fields, such as MRI and brain tumors. The
effectiveness and responsibility of this operator are investigated in this study. All of the simulations and
experimental data utilized in this study were produced using MATLAB R2023b.
The Sobel method uses the edge function's derivative approximation to identify image edges.
Consequently, it provides edges at the locations where the image's gradient is highest. The Sobel method,
which has dimensions of 33, uses the horizontal and vertical gradient matrices in edge detection
procedures. The outcomes demonstrate that the mathematical method of edge detection using simulation
with MATLAB software is an excellent method for picture analysis.
References
Hasibuan, A. H., Zebua, T., & Hondro, R. K. (2020). Penerapan Metode Sobel Edge Detection dan
Image Processing Untuk Mengetahui Diameter Apel Fuji Menggunakan Aplikasi Matlab.
JURIKOM (Jurnal Riset Komputer), 7(3), 450. https://doi.org/10.30865/jurikom.v7i3.2261.
Habeeb, M. S., & Babu, T. R. (2022). Network intrusion detection system: a survey on artificial
intelligence‐based techniques. Expert Systems, 39(9), e13066.
Naveen Sai Bommina, Uppu Lokesh, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar,
"Optimizing AI-Driven Security Protocols in IoT Networks Using Metaheuristic Algorithms",
International Journal of Intelligent Systems and Applications in Engineering, IJISAE, 2024,
(23s), 3339–3347.
Muhammad Rizky Alditra Utama, K., Umar, R., & Yuhdana, A. (2022). Edge detection
comparative analysis using Roberts, Sobel, Prewitt, and Canny methods. Jurnal Teknologi Dan
Sistem Komputer, 10(2), 67–71. https://doi.org/10.14710/jtsiskom.2022.14209.
Habeeb, M. S. (2024). Predictive analytics and cybersecurity. Intelligent Techniques for Predictive
Data Analytics, 151-169.
R. Gnanakumaran, Divya Rohatgi, A K Sampath, Nidhi Nagar, D. Amuthaguka, Raj Kumar Gupta,
"Robust Extreme Learning Machine based Sentiment Analysis and Classification", 2023 5th
International Conference on Smart Systems and Inventive Technology (ICSSIT), (2023), DOI:
1109/ICSSIT55814.2023.10061017.
Uppu Lokesh , Naveen Sai Bommina , Nandipati Sai Akash , Dr. Hussain Syed , Dr. Syed Umar.
(2021). Deep Reinforcement Learning with Genetic Algorithm Tuning for Intrusion Detection in
IoT Systems. International Journal of Communication Networks and Information Security
(IJCNIS), 13(3), 582–595.
Ayyed, D. J. (2020). Image Steganography Based Sobel Edge Detection Using FPGA. Technium,
(6), 23–34.
minimization
Tian, R., Sun, G., Liu, X., & Zheng, B. (2021). Sobel edge detection based on weighted nuclear
norm
image denoising. Electronics (Switzerland), 10(6), 1–15.
https://doi.org/10.3390/electronics10060655
Uppu Lokesh, Naveen Sai Bommina, Nandipati Sai Akash, Dr. Hussain Syed, Dr. Syed Umar,
"Designing Energy-Efficient and Secure IoT Architectures Using Evolutionary Optimization
Algorithms", International Journal of Applied Engineering & Technology, Vol. 4 No.2,
September, 2022.
Electrical
Younis, A. K., Younis, B. M. K., & Jarjees, M. S. (2022). Hardware implementation of Sobel edge
detection system for blood cells images-based field programmable gate array. Indonesian Journal
of
Engineering
and
https://doi.org/10.11591/ijeecs.v26.i1.pp86-95
Computer
Science,
(1),
–95.
Nandipati Sai Akash, Naveen Sai Bommina, Uppu Lokesh, Hussain Syed, Syed Umar,
"Optimized Block Chain-Enabled Security Mechanism for IoT Using Ant Colony Optimization",
International Journal on Recent and Innovation Trends in Computing and Communication,
(2023), 11(10), 1226–1233.
M. Mukhedkar, D. Rohatgi, V.A. Vuyyuru, K.V.S.S. Ramakrishna, Y.A. Baker El-Ebiary, V.A.
Asir Daniel, "Feline wolf net: A hybrid lion-grey wolf optimization deep learning model for
ovarian cancer detection", Int. J. Adv. Comput. Sci. Appl., 14 (9) (2023)
Pamungkas, P. G., Kusrini, K., & Fatta, H. Al. (2020). Deteksi Mobil Ambulance Menggunakan
Operator Sobel. Inspiration: Jurnal Teknologi Informasi Dan Komunikasi, 10(1), 87.
https://doi.org/10.35585/inspir.v10i1.2534
Habeeb, M. S., & Babu, T. R. (2024). Coarse and fine feature selection for network intrusion
detection systems (IDS) in IoT networks. Transactions on Emerging Telecommunications
Technologies, 35(4), e4961.
Divya Rohatgi, Dr. Tulika Pandey, "Regression Test Selection Framework for Web Services",
INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9,
ISSUE 03, MARCH 2020.
Asmaidi, A., Putra, D. S., Risky, M. M., & R, F. U. (2019). Implementation of Sobel Method
Based Edge Detection for Flower Image Segmentation. SinkrOn, 3(2), 161.
https://doi.org/10.33395/sinkron.v3i2.10050.
Baareh, A. K. M., Al-Jarrah, A., Smadi, A. M., & Shakah, G. H. (2018). Performance Evaluation
of Edge Detection Using Sobel, Homogeneity and Prewitt Algorithms. Journal of Software
Engineering and Applications, 11(11), 537–551. https://doi.org/10.4236/jsea.2018.1111032.
