CAMERA CONTROL ALGORITHM AND IMAGE QUALITY ASSESSMENT METHOD TO OBTAIN A QUALITY IMAGE
Article Sidebar
Main Article Content
Abstract
Since small-sized objects are expressed in the image with very few pixels and are located at a fairly large distance from the camera, their recognition by computer vision-supported systems becomes difficult. At this time, the issue of obtaining high-quality images of them becomes relevant. The object study is the camera and the images obtained from it. Existing methods have been studied and a new approach that can work faster to obtain high-quality images has been proposed. The subject of the research is a method for assessing the quality of the image and controlling the focus of the camera using existing tools in order to obtain a high-quality image. The purpose of the research is to create an algorithm for evaluating the image and controlling the camera device in order to obtain a high-quality image for a detection system supported by computer vision for small-sized objects. Improving the quality of the image with the proposed methods creates important conditions for the effective operation of recognition systems operating in real-time. As a result of the research, the method for assessing the image in terms of quality and the camera control algorithm for a high-quality image of the object is proposed. The rationale for the proposed main methods of research is given, the results of experimental studies of the proposed methods are presented, and the validity of the adopted theoretical conclusions is confirmed.
Article Details
References
Hashimov, E.G. and Khaligov, G. (2024), “The issue of training of the neural network for drone detection”, Advanced İnformation Systems, vol. 8, no. 3, pp. 53–58, doi: https://doi.org/10.20998/2522-9052.2024.3.06
Hashimov, E.G., Sabziev, E.N., Huseynov, B.S. and Huseynov, M.A. (2023), “Mathematical aspects of determining the motion parameters of a target by UAV”, Advanced Information Systems, vol. 7, no. 1, pp. 18–22, doi: https://doi.org/10.20998/2522-9052.2023.1.03
Hashimov, E.G. and Maharramov, R.R. (2025), “Taking control of dead zone of radiolocation station by the automatic acting electro-optic system”, Defence Science Journal, vol. 75, no. 1, pp. 84–89, doi: https://doi.org/10.14429/dsj.19950
Onuchukwu, C. C. and Ezenwa I. A. (2015), “Fundamentals of geometrıc and physıcal optıcs for undergraduates”, UR excellency prınts awka, available at: https://www.researchgate.net/publication/351601232
Stephan, C.N., Healy, S., Bultitude, H. and Glen, C. (2022), “Craniofacial superimposition: a review of focus distance estimation methods and an extension to profile view photographs”, International Journal of Legal Medicine, vol. 136, pp. 1697–1716, doi: https://doi.org/10.1007/s00414-022-02871-5
Jianyu, Y. (2022), Bistatic Synthetic Aperture Radar, pp. 217–246, doi: https://doi.org/10.1016/B978-0-12-822459-5.00005-0
Bong, D.B.L. and Khoo, B.E. (2014), “Blind image blur assessment by using valid reblur range and histogram shape difference”, Sig Process Image Commun., vol. 29, no. 6, pp. 699–710, doi: https://doi.org/10.1016/j.image.2014.03.003
Li L., Lin W., Wang X., Yang G., Bahrami K. and Kot A.C. (2016), “No-reference image blur assessment based on discrete orthogonal moments”, IEEE Trans Cybern., vol. 46, no. 1, pp. 39–50, doi: https://doi.org/10.1109/TCYB.2015.2392129
Wang, S., Deng, C., Zhao, B., Huang G.B. and Wang, B. (2016), “Gradient-based no-reference image blur assessment using extreme learning machine”, Neurocomputing, vol. 174, pp. 310–321, doi: https://doi.org/10.1016/j.neucom.2014.12.117
Li, L., Wu, D., Wu, J., Li, H., Lin, W. and Kot, A.C. (2016), “Image sharpness assessment by sparse representation”, IEEE Trans Multimed, vol. 18, no. 6, pp. 1085–1097, doi: https://doi.org/10.1109/TMM.2016.2545398
Popov, A., Vasilyeva, I., Kosharskyi, V. and Dergachov, K. (2023), “Selection of color contrast objects against a non-stationary background using modified HSV model”, 2023 IEEE 6th Int. Conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo, pp. 84–87, doi: https://doi.org/10.1109/UkrMiCo61577.2023.10380393
Yaloveha, V., Hlavcheva, D., Podorozhniak, A. and Kuchuk, H. (2019), “Fire hazard research of forest areas based on the use of convolutional and capsule neural networks”, 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering, UKRCON 2019 – Proceedings, DOI: http://dx.doi.org/10.1109/UKRCON.2019.8879867
Ferzli, R. and Karam, L. J. (2009), “A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)”, IEEE Trans. on Image Processing, vol. 18, no. 4, pp. 717–728, doi: https://doi.org/10.1109/TIP.2008.2011760
Petrovska, I., Kuchuk, H., Kuchuk, N., Mozhaiev, O., Pochebut, M. and Onishchenko, Yu. (2023), “Sequential Series-Based Prediction Model in Adaptive Cloud Resource Allocation for Data Processing and Security”, 2023 13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023, 13–15 October, Athens, Greece, code 197136, doi: https://doi.org/10.1109/DESSERT61349.2023.10416496
Dingquan, L. and Tingting, J. (2019), “Blur-Specific No-Reference Image Quality Assessment: A Classification and Review of Representative Methods”, Int. Conf. on Sensing and Imaging, pp. 45–68, doi: https://doi.org/10.1007/978-3-319-91659-0_4
Sobel, İ. (2014), History and definition of the Sobel Operator, available at:
Gonzalez, R. and Woods, R. (1992), Digital Image Processing, Addison Wesley, available at:
https://www.cl72.org/090imagePLib/books/Gonzales,Woods-Digital.Image.Processing.4th.Edition.pdf
Hasanov, A.H., Hashimov, E.G. and Zulfugarov, B.S. (2023), “Comparative analysis of the efficiency of various energy storages”, Advanced Information Systems, vol. 7, no. 3, pp. 74–80, doi: https://doi.org/10.20998/2522-9052.2023.3.11
Bilozerskyi, V., Dergachov, K. and Krasnov, L. (2023), “New methods for video data pre-processing to improve the quality of computer vision systems” 2023 IEEE 4th KhPI Week on Advanced Technology, KhPI Week 2023 - Conference Proceedings, doi: https://doi.org/10.1109/KhPIWeek61412.2023.10312988
Ibtisam, E., Al-Sabawi, E.A. and Younus, M.D. (2021), “Design of Fractional-order Sobel Filters for Edge Detections”, IOP Conf. Series: Materials Science and Engineering, vol. 1152, 012028, doi: https://doi.org/10.1088/1757-899X/1152/1/012028
Zhang K., Liao Q. “FPGA implementation of eightdirection Sobel edge detection algorithm based on adaptive threshold”, Journal of Physics: Conference Series 1678-012105, IOP Publishing. 2020. doi: https://doi.org/10.1088/1742-6596/1678/1/012105
Mohammad, E.J., Taha, R.Y. and Mazher, H.A. (2022), “Design Study Sobel Edge Detection of an Image”, Journal of Multidisciplinary Engineering Science and Technology, vol. 9, is. 3, available at: https://www.jmest.org/wp-content/uploads/JMESTN42354017.pdf
İbrahimov B.G., Hasanov A.H. and Hashimov E.G. (2024), “Research and analysis of efficiency indicators of critical infrastructures in the communication system”, Advanced Information Systems, vol. 8, no. 2, pp. 58–64, doi: https://doi.org/10.20998/2522-9052.2024.2.07
Kulik, A. and Dergachev, K. (2016), “Intelligent transport systems in aerospace engineering”, Studies in Systems, Decision and Control, vol. 32, Springer International Publ., Cham, pp. 243–303, doi: https://doi.org/10.1007/978-3-319-19150-8_8
Xiangxi, Z., Yonghui, Z., Shuaiyan, Z. and Jian, Z. (2018), “FPGA implementation of edge detection for Sobel operator in eight directions”, IEEE Asia Pacific Conference on Circuits and Systems, Chengdu., pp. 520–523, doi: https://ieeexplore.ieee.org/document/8605703