UNDERWATER OBJECT SHAPE DETECTION BASED ON TONAL DISTRIBUTION AND EDGE DETECTION USING DIGITAL IMAGE PROCESSING
Abstract
Underwater exploration activities always have their own charm, many exotic objects that exist in underwater ecosystems have not been mapped properly, due to the lack of related databases of the shapes and names of these underwater objects. Another factor that affects the visibility of objects related to the quantity of light intensity that enters under water, also not as much above the surface of the abundant water, especially during the day. This also hinders the process of documenting underwater objects. The main purpose of this study was to obtain the shape of underwater objects for several conditions of light intensity under water using a low cost digital image sensor camera. The method used in this research is to combine tonal distributions with object edge detection in digital image processing. The test results show that object detection tests in clear and turbid water can detect objects even though they are using a low-cost and low-resolution camera, but with the help of adequate lighting it can be done. From that it can be concluded that the detection of underwater objects is successful.
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Universitatea din Pitești, IEEE Romania Section, IEEE Industry Applications Society, and Institute of Electrical and Electronics Engineers, Proceedings of the 2014 6th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) : October 23-October 25, 2014, University of Pitesti.
S. S. Ranhotra, “Detection of salinity of sea water using image processing techniques,” 2014 Asia-Pacific Conference on Computer Aided System Engineering, APCASE 2014, pp. 76–81, Oct. 2014, doi: 10.1109/APCASE.2014.6924475.
C. Sharma, Isha, and V. Vashisht, “Water quality estimation using computer vision in UAV,” Proceedings of the Confluence 2021: 11th International Conference on Cloud Computing, Data Science and Engineering, pp. 448–453, Jan. 2021, doi: 10.1109/CONFLUENCE51648.2021.9377082.
R. Sulistyowati, A. Suryowinoto, A. Fahruzi, and M. Faisal, “Prototype of the Monitoring System and Prevention of River Water Pollution Based on Android,” in IOP Conference Series: Materials Science and Engineering, 2019. doi: 10.1088/1757-899X/462/1/012028.
A. Suryowinoto, T. Herlambang, R. Tsusanto, and F. A. Susanto, “Prototype of an Automatic Entrance Gate Security System Using a Facial Recognition Camera Based on The Haarcascade Method,” J Phys Conf Ser, vol. 2117, no. 1, p. 012015, 2021, doi: 10.1088/1742-6596/2117/1/012015.
D. Boulinguez and A. Quinquis, “Underwater buried object recognition using wavelet packets and Fourier descriptors,” Proceedings - International Conference on Image Analysis and Processing, ICIAP 1999, pp. 478–483, 1999, doi: 10.1109/ICIAP.1999.797641.
A. K. M. Baareh et al., “Performance Evaluation of Edge Detection Using Sobel, Homogeneity and Prewitt Algorithms,” Journal of Software Engineering and Applications, vol. 11, no. 11, pp. 537–551, Nov. 2018, doi: 10.4236/JSEA.2018.1111032.
L. Zhang and K. Yang, “Region-of-interest extraction based on frequency domain analysis and salient region detection for remote sensing image,” IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 5, pp. 916–920, May 2014, doi: 10.1109/LGRS.2013.2281827.
R. Szeliski, “Computer Vision: Algorithms and Applications,” 2010. [Online]. Available: http://szeliski.org/Book/.
T. Herlambang et al., “DEVELOPING DESIGN OF AUTOMATIC EGG QUALITY DETECTOR USING ROI AND RGB TEMPLATE METHODS,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 16, no. 2, pp. 569–582, Jun. 2022, doi: 10.30598/BAREKENGVOL16ISS2PP569-582.
Z. Xu, X. Ji, M. Wang, and X. Sun, “Edge detection algorithm of medical image based on Canny operator,” J Phys Conf Ser, vol. 1955, no. 1, p. 012080, Jun. 2021, doi: 10.1088/1742-6596/1955/1/012080.
L. Yang, X. Wu, D. Zhao, H. Li, and J. Zhai, “An improved Prewitt algorithm for edge detection based on noised image,” Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011, vol. 3, pp. 1197–1200, 2011, doi: 10.1109/CISP.2011.6100495.
R. Tian, G. Sun, X. Liu, and B. Zheng, “Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising,” Electronics 2021, Vol. 10, Page 655, vol. 10, no. 6, p. 655, Mar. 2021, doi: 10.3390/ELECTRONICS10060655.
G. Ravivarma, K. Gavaskar, D. Malathi, K. G. Asha, B. Ashok, and S. Aarthi, “Implementation of Sobel operator based image edge detection on FPGA,” Mater Today Proc, vol. 45, pp. 2401–2407, Jan. 2021, doi: 10.1016/J.MATPR.2020.10.825.
G. Chaple and R. D. Daruwala, “Design of Sobel operator based image edge detection algorithm on FPGA,” International Conference on Communication and Signal Processing, ICCSP 2014 - Proceedings, pp. 788–792, Nov. 2014, doi: 10.1109/ICCSP.2014.6949951.
G. N. Chaple, R. D. Daruwala, and M. S. Gofane, “Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA,” Proceedings - International Conference on Technologies for Sustainable Development, ICTSD 2015, Apr. 2015, doi: 10.1109/ICTSD.2015.7095920.
S. R. Allred, A. Radonjić, A. L. Gilchrist, and D. H. Brainard, “Lightness perception in high dynamic range images: Local and remote luminance effects,” J Vis, vol. 12, no. 2, pp. 1–16, 2012, doi: 10.1167/12.2.7.
I. R. Khan, W. Aziz, and S. O. Shim, “Tone-Mapping Using Perceptual-Quantizer and Image Histogram,” IEEE Access, vol. 8, pp. 31350–31358, 2020, doi: 10.1109/ACCESS.2020.2973273.
T. McREYNOLDS and D. BLYTHE, “Image Processing Techniques,” Advanced Graphics Programming Using OpenGL, pp. 211–245, Jan. 2005, doi: 10.1016/B978-155860659-3.50014-7.
P. Griffiths, S. Van Der Linden, T. Kuemmerle, and P. Hostert, “Erratum: A pixel-based landsat compositing algorithm for large area land cover mapping (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing),” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 6, no. 5, pp. 2088–2101, 2013, doi: 10.1109/JSTARS.2012.2228167.
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