Penerapan Kombinasi Algoritma Sobel dan Canny (SoCan) dalam Identifikasi Citra Inversi Albatros Laysan
Utilizing an edge detection algorithm in an image will produce the edges of the image object. The aim is to mark the part that becomes the image's detail and correct the point of blurring of vision that occurs due to errors or the effects of the image acquisition process. This study aims to see the ability of the combination of Sobel and Canny edge detection algorithms (SoCan) to detect the inverted image. The image dataset used is the image of the Laysan Albatross, which consists of 10 original images and ten images that have been inverted based on the standard image dataset. The Laysan albatross is a large species of seabird found in the North Pacific. 99.7% of the total population is found in the Northwest Hawaiian Islands. The research dataset was obtained from the Caltech Vision Lab website http://www.vision.caltech.edu/datasets/cub_200_2011/ with dimensions of 500 x 271 pixels. Based on the analysis of 10 experiments carried out, the combination of the Sobel and Canny algorithm (SoCan) is not good at performing edge detection because it only has an average accuracy of 47.79% with an average accuracy error rate of 52.21%. Thus, in this case, the combination of the Sobel and Canny algorithms (SoCan) is not able to identify the Inversion Image
H. Zhang, A. Jolfaei, and M. Alazab, “A Face Emotion Recognition Method Using Convolutional Neural Network and Image Edge Computing,” IEEE Access, vol. 7, pp. 159081–159089, 2019.
Y. Wang, Y. Yu, X. Zhu, and Z. Zhang, “Pattern recognition for measuring the flame stability of gas-fired combustion based on the image processing technology,” Fuel, vol. 270, no. 117486, pp. 1–13, 2020.
A. Latif et al., “Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review,” Mathematical Problems in Engineering, vol. 2019, pp. 1–21, 2019.
S. Hassanpour, N. Tomita, T. DeLise, B. Crosier, and L. A. Marsch, “Identifying substance use risk based on deep neural networks and Instagram social media data,” Neuropsychopharmacology, vol. 44, no. 3, pp. 487–494, 2019.
D. N. Trivedi, N. D. Shah, A. M. Kothari, and R. M. Thanki, “Dental image processing for human identification,” Dental Image Processing for Human Identification, pp. 1–81, 2019.
L. H. Gong, C. Tian, W. P. Zou, and N. R. Zhou, “Robust and imperceptible watermarking scheme based on Canny edge detection and SVD in the contourlet domain,” Multimedia Tools and Applications, vol. 80, no. 1, pp. 439–461, 2021.
B. Watkins and A. van Niekerk, “A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery,” Computers and Electronics in Agriculture, vol. 158, no. November 2018, pp. 294–302, 2019.
R. G. Zhou, H. Yu, Y. Cheng, and F. X. Li, “Quantum image edge extraction based on improved Prewitt operator,” Quantum Information Processing, vol. 18, no. 261, pp. 1–24, 2019.
M. Versaci and F. C. Morabito, “Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence,” International Journal of Fuzzy Systems, vol. 23, pp. 918–936, 2021.
G. Chen, Z. Jiang, and M. M. Kamruzzaman, “Radar remote sensing image retrieval algorithm based on improved Sobel operator,” Journal of Visual Communication and Image Representation, vol. 71, no. 102720, pp. 1–8, 2020.
Erwin and T. Yuningsih, “Detection of Blood Vessels in Optic Disc with Maximum Principal Curvature and Wolf Thresholding Algorithms for Vessel Segmentation and Prewitt Edge Detection and Circular Hough Transform for Optic Disc Detection,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 9, pp. 1–12, 2020.
M. Yasir et al., “Automatic Coastline Extraction and Changes Analysis Using Remote Sensing and GIS Technology,” IEEE Access, vol. 8, pp. 180156–180170, 2020.
B. Iqbal, W. Iqbal, N. Khan, A. Mahmood, and A. Erradi, “Canny edge detection and Hough transform for high resolution video streams using Hadoop and Spark,” Cluster Computing, vol. 23, no. 1, pp. 397–408, 2020.
Y. Cho et al., “Keypoint Detection Using Higher Order Laplacian of Gaussian,” IEEE Access, vol. 8, pp. 10416–10425, 2020.
S. Rahmawati, R. Devita, R. H. Zain, E. Rianti, N. Lubis, and A. Wanto, “Prewitt and Canny Methods on Inversion Image Edge Detection: An Evaluation,” Journal of Physics: Conference Series, vol. 1933, no. 1, 2021.
D. N. Lohare, R. R. Manza, and N. Tiwari, “Comparative Study of Prewitt and Canny Edge Detector Using Image Processing Techniques,” Advances in Intelligent Systems and Computing, vol. 1187, pp. 705–713, 2021.
B. K. Shah, V. Kedia, R. Raut, S. Ansari, and A. Shroff, “Evaluation and Comparative Study of Edge Detection Techniques,” IOSR Journal of Computer Engineering, vol. 22, no. 5, pp. 6–15, 2020.
A. Wanto, S. D. Rizki, S. Andini, S. Surmayanti, N. L. W. S. R. Ginantra, and H. Aspan, “Combination of Sobel+Prewitt Edge Detection Method with Roberts+Canny on Passion Flower Image Identification,” Journal of Physics: Conference Series, vol. 1933, no. 012037, pp. 1–8, 2021.
P. Vinista and M. M. Joe, “A Novel Modified Sobel Algorithm for Better Edge Detection of Various Images,” International Journal of Emerging Technologies in Engineering Research (IJETER), vol. 7, no. 3, pp. 25–31, 2019.
Resdiana Hutagalung, “Mendeteksi Tepi Citra Penyakit Hemokromatosis Dengan Menggunakan Metode Log (Laplacian Of Gaussian),” JUKI : Jurnal Komputer dan Informatika, vol. 2, no. 1, pp. 49–58, 2020.
R. A. Saputra, Reskal, and F. M. Wahyuni, “Segmentasi Pada Plat Kendaraan Dinas dengan Metode Deteksi Tepi Canny, Prewitt, Sobel, & Roberts,” Jurnal Sains Komputer & Informatika (J-SAKTI), vol. 6, no. 1, pp. 328–339, 2022.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “CUB-200-2011,” CaltechDATA, 2022. [Online]. Available: https://data.caltech.edu/records/20098. [Accessed: 11-Apr-2022].
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