Published: 2025-07-01
Classification of Image Corner Point Detection Systems to Identify a Shape
DOI: 10.35870/jtik.v9i3.3824
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Abstract
StudyThis contains about the detection of circular object images. The circular object tested is the moon image object, the moon image was chosen because the moon image has various moon shapes, namely the full moon, half moon and crescent moon. To detect the shape of the circular object image, several stages are carried out by starting the image segmentation process. (1) The segmentation process using the bi-level thresholding method makes the image black and white, (2) after that the image is repaired with the morphological process of the opening and closing methods. (3) For training data, the shape extraction process is carried out, namely the circular nature of the object (circularity) to determine the roundness of an object. For the testing process, the same process is also carried out as the process of obtaining circular image detection.
Keywords
Circle Image Detection ; Bi-Level Thresholding ; Opening ; Closing ; Extractioncircularity Form
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 9 No. 3 (2025)
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Section: Computer & Communication Science
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Published: %750 %e, %2025
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License: CC BY 4.0
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Copyright: © 2025 Authors
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DOI: 10.35870/jtik.v9i3.3824
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Dadang Iskandar Mulyana
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Fiktor Kurnia Tofano
Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
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Aznan, A. A., Ruslan, R., Rukunudin, I. H., Azizan, F. A., & Hashim, A. Y. (2017). Rice seed varieties identification based on extracted colour features using image processing and artificial neural network (ANN). Int. J. Adv. Sci. Eng. Inf. Technol, 7(6), 2220-2225.
-
Bansal, M., Kumar, M., Kumar, M., & Kumar, K. (2021). An efficient technique for object recognition using Shi-Tomasi corner detection algorithm. Soft Computing, 25(6), 4423-4432.
-
Cuevas, E., Díaz-Cortes, M. A., & Mezura-Montes, E. (2019). Corner detection of intensity images with cellular neural networks (CNN) and evolutionary techniques. Neurocomputing, 347, 82-93. https://doi.org/10.1016/j.neucom.2019.03.014.
-
Jeong, K., & Moon, H. (2011, May). Object detection using FAST corner detector based on smartphone platforms. In 2011 First ACIS/JNU International Conference on Computers, Networks, Systems and Industrial Engineering (pp. 111-115). IEEE. https://doi.org/10.1109/CNSI.2011.60.
-
Jing, J., Liu, C., Zhang, W., Gao, Y., & Sun, C. (2023). ECFRNet: Effective corner feature representations network for image corner detection. Expert Systems with Applications, 211, 118673. https://doi.org/10.1016/j.eswa.2022.118673.
-
Kahaki, S. M. M., Nordin, M. J., & Ashtari, A. H. (2014). Contour-based corner detection and classification by using mean projection transform. Sensors, 14(3), 4126-4143. https://doi.org/10.3390/s140304126.
-
Kaur, A., Kumar, M., & Jindal, M. K. (2022). Shi-Tomasi corner detector for cattle identification from muzzle print image pattern. Ecological Informatics, 68, 101549. https://doi.org/10.1016/j.ecoinf.2021.101549.
-
Parks, D., & Gravel, J. P. (2004). Corner detection. International Journal of Computer Vision, 1-17.
-
Shpitalni, M., & Lipson, H. (1997). Classification of sketch strokes and corner detection using conic sections and adaptive clustering.
-
Stegmann, M. B., & Gomez, D. D. (2002). A brief introduction to statistical shape analysis. Informatics and mathematical modelling, Technical University of Denmark, DTU, 15(11).
-
Valiammal, N., & Gethalaksmi, S. N. (2012). An optimal feature subset selection for leaf analysis. International Journal of Computer and Communication Engineering, 6.
-
Zakariah, M., AlShalfan, K., Li, D., Huang, W., Xu, G., Zhang, T., & Shahnasser, H. (2020). Image boundary, corner, and edge detection: past, present, and future. International Journal of Computer Electrical Engineering, 12(2), 39.
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