Design of an embedded machine vision system for smart cameras

[1] Zhang, Y., H. Yang and G. Wang, Monitoring and management of high-end tourism in protected areas based on 3D sensor image collection. Displays, 2021. 70: p. 102089. Search in Google Scholar

[2] Mantzaris, A.V., et al., Tagasaurus, a tool to assist manual image tagging and the creation of image collections. Software Impacts, 2021. 10: p. 100157. Search in Google Scholar

[3] Vithu, P. and J.A. Moses, Machine vision system for food grain quality evaluation: A review. Trends in Food Science & Technology, 2016. 56: p. 13-20. Search in Google Scholar

[4] Rehman, T.U., et al., Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture, 2019. 156: p. 585-605. Search in Google Scholar

[5] Javaid, M., et al., Exploring impact and features of machine vision for progressive industry 4.0 culture. Sensors International, 2022. 3: p. 100132. Search in Google Scholar

[6] Hitelman, A., et al., Biometric identification of sheep via a machine-vision system. Computers and Electronics in Agriculture, 2022. 194: p. 106713. Search in Google Scholar

[7] Borges Oliveira, D.A., et al., A review of deep learning algorithms for computer vision systems in livestock. Livestock Science, 2021. 253: p. 104700. Search in Google Scholar

[8] Vandone, A., et al., 3D vision system integration on Additive Manufacturing machine for in-line part inspection. Procedia CIRP, 2020. 95: p. 72-77. Search in Google Scholar

[9] Lins, R.G., P.R.M. de Araujo and M. Corazzim, In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems. Robotics and Computer-Integrated Manufacturing, 2020. 61: p. 101859. Search in Google Scholar

[10] Kotenko, I., et al., Ensuring the survivability of embedded computer networks based on early detection of cyber attacks by integrating fractal analysis and statistical methods. Microprocessors and Microsystems, 2022. 90: p. 104459. Search in Google Scholar

[11] Smith, B.R., F. Spooner and C.L. Wood, Using embedded computer-assisted explicit instruction to teach science to students with autism spectrum disorder. Research in Autism Spectrum Disorders, 2013. 7(3): p. 433-443. Search in Google Scholar

[12] Ma, F., Action recognition of dance video learning based on embedded system and computer vision image. Microprocessors and Microsystems, 2021. 81: p. 103779. Search in Google Scholar

[13] Qu, C., J. Shao and Z. Cheng, Can embedding in global value chain drive green growth in China’s manufacturing industry? Journal of Cleaner Production, 2020. 268: p. 121962. Search in Google Scholar

[14] Kotenko, I., et al., Ensuring the survivability of embedded computer networks based on early detection of cyber attacks by integrating fractal analysis and statistical methods. Microprocessors and Microsystems, 2022. 90: p. 104459. Search in Google Scholar

[15] Karaman, O., A. Alhudhaif and K. Polat, Development of smart camera systems based on artificial intelligence network for social distance detection to fight against COVID-19. Applied Soft Computing, 2021. 110: p. 107610. Search in Google Scholar

[16] Carey, S.J., D.R.W. Barr and P. Dudek, Low power high-performance smart camera system based on SCAMP vision sensor. Journal of Systems Architecture, 2013. 59(10): p. 889-899. Search in Google Scholar

[17] Ahmad, J., et al., Embedded deep vision in smart cameras for multi-view objects representation and retrieval. Computers & Electrical Engineering, 2017. 61: p. 297-311. Search in Google Scholar

[18] Athanasopoulou, L., A. Papacharalampopoulos and P. Stavropoulos, Context awareness system in the use phase of a smart mobility platform: A vision system for a light-weight approach. Procedia CIRP, 2020. 88: p. 560-564. Search in Google Scholar

[19] Jing, Y., et al., Numerical study of a CNN-based model for regional wave prediction. Ocean Engineering, 2022. 255: p. 111400. Search in Google Scholar

[20] Chaabouni, S., J. Benois-Pineau and C. Ben Amar, ChaboNet: Design of a deep CNN for prediction of visual saliency in natural video. Journal of Visual Communication and Image Representation, 2019. 60: p. 79-93. Search in Google Scholar

[21] Baisa, N.L., Occlusion-robust online multi-object visual tracking using a GM-PHD filter with CNN-based re-identification. Journal of Visual Communication and Image Representation, 2021. 80: p. 103279. Search in Google Scholar

[22] Xiao, L., et al., A Simplified CNNs Visual Perception Learning Network Algorithm for Foods Recognition. Computers & Electrical Engineering, 2021. 92: p. 107152. Search in Google Scholar

[23] Zhang, J., K. Li and Z. Wang, Parallel-fusion LSTM with synchronous semantic and visual information for image captioning. Journal of Visual Communication and Image Representation, 2021. 75: p. 103044. Search in Google Scholar

[24] Zheng, X. and W. Chen, An Attention-based Bi-LSTM Method for Visual Object Classification via EEG. Biomedical Signal Processing and Control, 2021. 63: p. 102174. Search in Google Scholar

[25] Su, J., et al., BERT-hLSTMs: BERT and hierarchical LSTMs for visual storytelling. Computer Speech & Language, 2021. 67: p. 101169. Search in Google Scholar

[26] Aslan, M.F., et al., COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization. Computers in Biology and Medicine, 2022. 142: p. 105244. Search in Google Scholar

[27] Kamalraj, R., et al., Interpretable filter based convolutional neural network (IF-CNN) for glucose prediction and classification using PD-SS algorithm. Measurement, 2021. 183: p. 109804. Search in Google Scholar

[28] Mousouliotis, P.G. and L.P. Petrou, CNN-Grinder: From Algorithmic to High-Level Synthesis descriptions of CNNs for Low-end-low-cost FPGA SoCs. Microprocessors and Microsystems, 2020. 73: p. 102990. Search in Google Scholar

[29] Lindemann, B., et al., A survey on anomaly detection for technical systems using LSTM networks. Computers in Industry, 2021. 131: p. 103498. Search in Google Scholar

[30] Yan, Z., et al., An effective compression algorithm for real-time transmission data using predictive coding with mixed models of LSTM and XGBoost. Neurocomputing, 2021. 462: p. 247-259. Search in Google Scholar

[31] Che, C., et al., Combining multiple deep learning algorithms for prognostic and health management of aircraft. Aerospace Science and Technology, 2019. 94: p. 105423. Search in Google Scholar

[32] Zhi, Z., D. Liu and L. Liu, A performance compensation method for GPS/INS integrated navigation system based on CNN–LSTM during GPS outages. Measurement, 2022. 188: p. 110516. Search in Google Scholar

[33] Li, H., et al., Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network. Biomedical Signal Processing and Control, 2022. 72: p. 103342. Search in Google Scholar

[34] Shen, Z., et al., Wind speed prediction of unmanned sailboat based on CNN and LSTM hybrid neural network. Ocean Engineering, 2022. 254: p. 111352. Search in Google Scholar