Abstract
A new approach to computing the gradient characteristics of a grayscale image as an array of features of an object of interest is considered. It is proposed to design a model of a reconfigurable computing environment that can simultaneously process each pixel of the source image in parallel mode and generate an array with gradient characteristics. Due to the architectural principles of model construction, the gradient is computed in a single clock cycle of the elementary calculator of the reconfigurable computing environment.
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REFERENCES
J. Chen and X. Ran, ‘‘Deep learning with edge computing: A review,’’ Proc. IEEE 107, 1655–1674 (2019). https://doi.org/10.1109/JPROC.2019.2921977
E. Arnold, O. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis, ‘‘A survey on 3D object detection methods for autonomous driving applications,’’ IEEE Trans. Intell. Transp. Syst. 20, 3782–3795 (2019). https://doi.org/10.1109/TITS.2019.2892405
D. Li, D. Zhao, Q. Zhang, and Y. Chen, ‘‘Reinforcement learning and deep learning based lateral control for autonomous driving [Application notes],’’ IEEE Comput. Intell. Mag. 14, 83–98 (2019). https://doi.org/10.1109/MCI.2019.2901089
C. You, J. Lu, D. Filev, and P. Tsiotras, ‘‘Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning,’’ Rob. Auton. Syst. 114, 1–18 (2019). https://doi.org/10.1016/j.robot.2019.01.003
N. C. Luong, D. T. Hoang, S. Gong, D. Niyato, P. Wang, Y.-C. Liang, and D. I. Kim, ‘‘Applications of deep reinforcement learning in communications and networking: A survey,’’ IEEE Commun. Surv. Tutorials 21, 3133–3174 (2019). https://doi.org/10.1109/COMST.2019.2916583
V. A. Surin and A. N. Tyrsin, ‘‘Nonlinear filter model for digital imaging of contrast images,’’ Optoelectron., Instrum. Data Process. 54, 155–161 (2018). https://doi.org/10.3103/S8756699018020061
J. S. Pershina, S. Ya. Kazdorf, and A. V. Lopota, ‘‘Methods of mobile robot visual navigation and environment mapping,’’ Optoelectron., Instrum. Data Process. 55, 181–188 (2019). https://doi.org/10.3103/S8756699019020109
X. Feng, Y. Jiang, X. Yang, M. Du, and X. Li, ‘‘Computer vision algorithms and hardware implementations: A survey,’’ Integration 69, 309–320 (2019). https://doi.org/10.1016/j.vlsi.2019.07.005
H. Nakahara, H. Yonekawa, T. Fujii, and S. Sato, ‘‘A lightweight YOLOv2: A binarized CNN with a parallel support vector regression for an FPGA,’’ in Proc. of the ACM/SIGDA Int. Symp. on Field-Programmable Gate Arrays, Monterey, USA, 2018, pp. 31–40. https://doi.org/10.1145/3174243.3174266
K. F. Lysakov, K. K. Oblaukhov, and M. Yu. Shadrin, ‘‘Implementation of FPGA algorithms for identification of image distortion due to compression,’’ Optoelectron., Instrum. Data Process. 56, 28–32 (2020). https://doi.org/10.3103/S8756699020010045
N. Dalal and B. Triggs, ‘‘Histograms of oriented gradients for human detection,’’ in Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, San Diego, USA, 2005, pp. 886–893. https://doi.org/10.1109/CVPR.2005.177
J. H. Luo, C. H. Lin, and X. Li, ‘‘Pure FPGA implementation of an HOG based real-time pedestrian detection system,’’ Sensors 18, 1174 (2018). https://doi.org/10.3390/s18041174
S. V. Shidlovskiy, Automatic Control: Reconfigurable Structures (Tomsk Gos. Univ., Tomsk, 2006).
I. A. Kalyaev, I. I. Levin, E. A. Semernikov, and V. I. Shmoilov, Reconfigurable Multi-Pipeline Computational Structures (Izd-vo YuNTs RAN, Rostov-on-Don, 2008).
V. G. Khoroshevsky, M. G. Kurnosov, and S. N. Mamoilenko, ‘‘Geographically-distributed multicluster computer system: Architecture and software,’’ Tomsk State Univ. J. Control Comput. Sci., No. 1, 79–84 (2011).
D. V. Shashev and S. V. Shidlovskiy, ‘‘Morphological processing of binary images using reconfigurable computing environments,’’ Optoelectron., Instrum. Data Process. 51, 227–233 (2015). https://doi.org/10.3103/S8756699015030036
S. V. Shidlovskiy, Automatic Control: Reconfigurable Structures in Systems with Distributed Parameters (Tomsk Gos. Univ., Tomsk, 2007).
L. Mennel, J. Symonowicz, S. Wachter, D. K. Polyushkin, A. J. Molina-Mendoza, and T. Mueller, ‘‘Ultrafast machine vision with 2D material neural network image sensors,’’ Nature 579, 62–66 (2020). https://doi.org/10.1038/s41586-020-2038-x
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The work is supported by the Russian Foundation for Basic Research, project no. 19-37-90110.
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Translated by E. Oborin
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Bondarchuk, A.S., Shashev, D.V. & Shidlovskiy, S.V. Design of a Model of a Reconfigurable Computing Environment for Determining Image Gradient Characteristics. Optoelectron.Instrument.Proc. 57, 132–140 (2021). https://doi.org/10.3103/S8756699021020047
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DOI: https://doi.org/10.3103/S8756699021020047