Abstract
Computer vision technologies have altered Product design and development, which have replaced old manual procedures. This study thoroughly examines computer vision’s role in affecting product design, examining its historical history, applications, and ramifications. The background emphasizes the constraints of traditional design, underlining the necessity for creative alternatives. Integrating computer vision aligns with Industry 4.0 trends, which call for smart and automated design procedures. The investigation delves into the growth of computer vision, its applications in quality control, design optimization, augmented/virtual reality, user interface design, and cultural/language acquisition. The article also looks into the relationship between computer vision and consumer analysis. The content analysis reduces 285 author keywords to 31 interrelated keywords that comprise five clusters. The conclusion emphasizes the social effect, promising more accessible, efficient, and innovative design processes. This multidisciplinary study dives into computer vision in product design and development by doing a thorough analysis of a variety of datasets. Examining three datasets with 285, 1066, and 1190 terms yields important results. The findings highlight the importance of “Product Design” and “Computer Vision” with changing patterns and concentrations across datasets. Thematic studies reveal repeating focus elements in titles and abstracts, such as “Design” and “Vision,” indicating a technological emphasis, human-centric concerns, and practical consequences. Network investigations reveal complex linkages and clustering within keyword networks, allowing for more in-depth knowledge of specific areas. These findings help to understand the dynamic interaction of computer vision and product design, driving future research and innovation in this rapidly growing sector.
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Zhou, L., Zhang, L., Konz, N.: Computer vision techniques in manufacturing. IEEE Trans. Syst. Man Cybern. Syst. 53, 105–117 (2023). https://doi.org/10.1109/TSMC.2022.3166397
Yang, Y., Deng, H.: EPformer: an efficient transformer-based approach for retail product detection in fisheye images. J. Electron. Imaging (2023). https://doi.org/10.1117/1.JEI.32.1.013017
Wang, Y., Han, Y., Chen, J., Wang, Z., Zhong, Y.: An FPGA-based hardware low-cost, low-consumption target-recognition and sorting system. World Electr. Veh. J. (2023). https://doi.org/10.3390/wevj14090245
Moragane, H.P.M.N.L.B., Perera, B.A.K.S., Palihakkara, A.D., Ekanayake, B.: Application of computer vision for construction progress monitoring: a qualitative investigation. Constr. Innov.Innov. (2022). https://doi.org/10.1108/CI-05-2022-0130
Sönmez, N.O.: A review of the use of examples for automating architectural design tasks. CAD Comput. Aid. Des. 96, 13–30 (2018). https://doi.org/10.1016/j.cad.2017.10.005
López, A., Valveny, E., Villanueva, J.J.: Real-time quality control of surgical material packaging by artificial vision. Assem. Autom. 25, 223–229 (2005). https://doi.org/10.1108/01445150510610944
Zhu, D., Liu, G.: Deep neural network model-assisted reconstruction and optimization of Chinese characters in product packaging graphic patterns and visual styling design. Sci. Program. (2022). https://doi.org/10.1155/2022/1219802
Su, Z., Yu, S., Chu, J., Zhai, Q., Gong, J., Fan, H.: A novel architecture: using convolutional neural networks for Kansei attributes automatic evaluation and labeling. Adv. Eng. Inf. (2020). https://doi.org/10.1016/j.aei.2020.101055
Rački, D., Tomaževič, D., Skočaj, D.: Detection of surface defects on pharmaceutical solid oral dosage forms with convolutional neural networks. Neural Comput. Appl. 34, 631–650 (2022). https://doi.org/10.1007/s00521-021-06397-6
Xiao, K., Ni, T.: Computer-aided industrial product design based on image enhancement algorithm and convolutional neural network. Comput.-Aid. Des. Appl. 21, 92–106 (2024). https://doi.org/10.14733/cadaps.2024.S3.92-106
Taati, B., Snoek, J., Mihailidis, A.: Video analysis for identifying human operation difficulties and faucet usability assessment. Neurocomputing 100, 163–169 (2013)
Pace, B., Cavallo, D.P., Cefola, M., Colella, R., Attolico, G.: Adaptive self-configuring computer vision system for quality evaluation of fresh-cut radicchio. Innov. Food Sci. Emerg. Technol.. Food Sci. Emerg. Technol. 32, 200–207 (2015). https://doi.org/10.1016/j.ifset.2015.10.001
Kapetanios, E.: Quo Vadis computer science: from turing to personal computer, personal content and collective intelligence. Data Knowl. Eng. 67, 286–292 (2008)
Deshpande, A., Patavardhan, P., Rao, D.H.: Super resolution based low cost vision system. In: Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2015 (2016)
Mukhanov, S.B., Uskenbayeva, R.: Pattern recognition with using effective algorithms and methods of computer vision library. In: Proceedings of the Advances in Intelligent Systems and Computing, pp. 810–819 (2020)
Ye, Z., Yin, H., Ye, Y.: Multiple scale comparative analysis of classical, dynamic and intelligent edge detection schemes. In: Proceedings of the Communications in Computer and Information Science, pp. 207–221 (2023)
Banerjee, D., Yu, K., Aggarwal, G.: Object tracking test automation using a robotic arm. IEEE Access 6, 56378–56394 (2018). https://doi.org/10.1109/ACCESS.2018.2873284
Wu, X., Wang, X.: Stable line and circle detection method in noise image for machine vision. In: Proceedings of the 2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021, pp. 1277–1282 (2021)
Yang, B., Du, X., Fang, Y., Li, P., Wang, Y.: Review of rigid object pose estimation from a single image. J. Image Graph. 26, 334–354 (2021). https://doi.org/10.11834/jig.200037
Avanzato, R.L.: Deep learning projects for multidisciplinary engineering design students. In: Proceedings of the ASEE Annual Conference and Exposition, Conference Proceedings (2023)
Chugh, G., Kumar, S., Singh, N.: MSTLA: multi-stage transfer learning approach for breast carcinoma diagnosis. In: Proceedings of the 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023, pp. 509–514 (2023)
Solunke, B.R., Gengaje, S.R.: A Review on traditional and deep learning based object detection methods. In: Proceedings of the 2023 International Conference on Emerging Smart Computing and Informatics, ESCI 2023 (2023)
Baur, C., Albarqouni, S., Demirci, S., Navab, N., Fallavollita, P.: Cathnets: detection and single-view depth prediction of catheter electrodes. In: Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 38–49 (2016)
Cheng, J., Li, H., Li, D., Hua, S., Sheng, V.S.: A survey on image semantic segmentation using deep learning techniques. Comput. Mater. Contin. 74, 1941–1957 (2023). https://doi.org/10.32604/cmc.2023.032757
Minaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44, 3523–3542 (2022). https://doi.org/10.1109/TPAMI.2021.3059968
Restrepo-Rodríguez, A.O., Casas-Mateus, D.E., Gaona-García, P.A., Montenegro-Marín, C.E.: Image recognition model over augmented reality based on convolutional neural networks through color-space segmentation. In: Proceedings of the Advances in Intelligent Systems and Computing, pp. 326–338 (2020)
Alsalamah, M.S.I.: Automatic face mask identification in saudi smart cities: using technology to prevent the spread of COVID-19. Inf. Sci. Lett. 12, 2411–2422 (2023). https://doi.org/10.18576/isl/120617
Tsai, Y., Wei, C.C.: Accelerated disaster reconnaissance using automatic traffic sign detection with UAV and AI. In: Proceedings of the Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience—Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, pp. 405–411 (2019)
Yamini, B., Jayaprakash, M., Logesswari, S., Ulagamuthalvi, V., Porselvi, R., Uthayakumar, G.S.: Enhanced Expectation-maximization algorithm for smart traffic IoT systems using deep generative adversarial networks to reduce waiting time. In: Proceedings of the 2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023—Proceedings, pp. 380–385 (2023)
Zheng, Q., Hou, Y., Yang, H., Tan, P., Shi, H., Xu, Z., Ye, Z., Chen, N., Qu, X., Han, X., et al.: Towards a sustainable monitoring: a self-powered smart transportation infrastructure skin. Nano Energy (2022). https://doi.org/10.1016/j.nanoen.2022.107245
Trenz, M., Berger, B: Analyzing online customer reviews-an interdisciplinary literature review and research agenda (2013)
Siva Subramanian, R., Girija, P., Anuradha, M., Dinesh, M.G., Aswini, J., Divya, P.: Heterogeneous ensemble variable selection to improve customer prediction using naive bayes model. Int. J. Recent. Innov. Trend. Comput. Commun. 11, 64–71 (2023). https://doi.org/10.17762/ijritcc.v11i5s.6599
Saba, N.S., Gandhi, R., Rajendran, S., Abraham, N.D.: Revolutionizing digital marketing using machine learning. In: Contemporary Approaches of Digital Marketing and the Role of Machine Intelligence; IGI Global, pp. 1–22 (2023)
Alves Gomes, M., Meisen, T.: A review on customer segmentation methods for personalized customer targeting in e-commerce use cases. Inf. Syst. e-Bus. Manage. 21, 527–570 (2023). https://doi.org/10.1007/s10257-023-00640-4
Sioma, A.: Vision system in product quality control systems. Appl. Sci. 13, 751 (2023)
Živković, M., Žujović, M., Milošević, J.: Architectural 3D-printed structures created using artificial intelligence: a review of techniques and applications. Appl. Sci. (2023). https://doi.org/10.3390/app131910671
Wang, W.C., Ahn, E., Feng, D., Kim, J.: A review of predictive and contrastive self-supervised learning for medical images. Mach. Intell. Res. 20, 483–513 (2023). https://doi.org/10.1007/s11633-022-1406-4
Khalil, C., Zarabi, S., Kirkham, K., Soni, V., Li, Q., Huszti, E., Yadollahi, A., Taati, B., Englesakis, M., Singh, M.: Validity of non-contact methods for diagnosis of obstructive sleep Apnea: a systematic review and meta-analysis. J. Clin. Anesth.Clin. Anesth. (2023). https://doi.org/10.1016/j.jclinane.2023.111087
Martins, J.R., Lambe, A.B.: Multidisciplinary design optimization: a survey of architectures. AIAA J. 51, 2049–2075 (2013)
Kelley, T.R.: Optimization, an important stage of engineering design. Technol. Teach. 69, 18 (2010)
Wang, G.G.: Definition and review of virtual prototyping. J. Comput. Inf. Sci. Eng. 2, 232–236 (2002)
Allmacher, C., Dudczig, M., Klimant, P., Putz, M.: Virtual prototyping technologies enabling resource-efficient and human-centered product development. Proc. Manuf. 21, 749–756 (2018)
Aromaa, S., Leino, S.-P., Viitaniemi, J.: Virtual prototyping in human-machine interaction design. VTT Technol. 185 (2014)
Yang, M.-T., Liao, W.-C.: Computer-assisted culture learning in an online augmented reality environment based on free-hand gesture interaction. IEEE Trans. Learn. Technol. 7, 107–117 (2014)
Mehmood, K., Bao, Y., Cheng, W., Khan, M.A., Siddique, N., Abrar, M.M., Soban, A., Fahad, S., Naidu, R.: Predicting the quality of air with machine learning approaches: current research priorities and future perspectives. J. Clean. Prod. 379, 134656 (2022). https://doi.org/10.1016/j.jclepro.2022.134656
Company, P., Varley, P.A., Plumed, R.: An algorithm for grouping lines which converge to vanishing points in perspective sketches of polyhedra. In Proceedings of the Graphics Recognition. Current Trends and Challenges: 10th International Workshop, GREC 2013, Bethlehem, PA, USA, August 20–21, 2013, Revised Selected Papers 10, pp. 77–95 (2014)
Zhang, D., Lee, D.-J., Taylor, B.: Seeing Eye Phone: a smart phone-based indoor localization and guidance system for the visually impaired. Mach. Vis. Appl. 25, 811–822 (2014)
Heimberger, M., Horgan, J., Hughes, C., McDonald, J., Yogamani, S.: Computer vision in automated parking systems: design, implementation and challenges. Image Vis. Comput. 68, 88–101 (2017)
Sönmez, N.O.: A review of the use of examples for automating architectural design tasks. Comput. Aided Des. 96, 13–30 (2018)
Machado, A., Veras, R., Aires, K., Neto, L.D.S.B.: A systematic review on product recognition for aiding visually impaired people. IEEE Latin Am. Trans. 19, 592–603 (2021)
Lu, G.: Construction of home product design system based on self-encoder depth neural network. Comput. Intell. Neurosci.. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/8331504
Li, R., Wang, C.: Cultural and creative product design and image recognition based on deep learning. Comput. Intell. Neurosci.. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/7256584
Zhang, G., Liu, J.: Intelligent vehicle modeling design based on image processing. Int. J. Adv. Rob. Syst. 18, 1729881421993347 (2021)
Su, Z., Yu, S., Chu, J., Zhai, Q., Gong, J., Fan, H.: A novel architecture: Using convolutional neural networks for Kansei attributes automatic evaluation and labeling. Adv. Eng. Inform. 44, 101055 (2020)
Wang, W., Zou, J., Fang, Y.: Design and evaluation of a somatosensory hat: an emotional semantic perspective. AATCC J. Res. 8, 20–29 (2021)
Abbas, A., Chalup, S.: Affective analysis of visual scenes using face pareidolia and scene-context. Neurocomputing 437, 72–83 (2021)
He, Y., Zhang, Z., Nan, X., Zhang, N., Guo, F., Rosales, E., Guan, L.: vConnect: perceive and interact with real world from CAVE. Multimed. Tools Appl. 76, 1479–1508 (2017)
Mehmood, K., Chang, S., Yu, S., Wang, L., Li, P., Li, Z., Liu, W., Rosenfeld, D., Seinfeld, J.H.: Spatial and temporal distributions of air pollutant emissions from open crop straw and biomass burnings in China from 2002 to 2016. Environ. Chem. Lett. 16, 301–309 (2018). https://doi.org/10.1007/s10311-017-0675-6
Yang, X., He, H., Wu, Y., Tang, C., Chen, H., Liang, J.: User intent perception by gesture and eye tracking. Cogent Eng. 3, 1221570 (2016)
Papadopoulos, S.-I., Koutlis, C., Papadopoulos, S., Kompatsiaris, I.: Multimodal Quasi-autoregression: forecasting the visual popularity of new fashion products. Int. J. Multimed. Inf. Retr. 11, 717–729 (2022)
Wang, Q., Liu, X., Liu, W., Liu, A.-A., Liu, W., Mei, T.: Metasearch: Incremental product search via deep meta-learning. IEEE Trans. Image Process. 29, 7549–7564 (2020)
Wang, Q., Liu, X., Liu, W., Liu, A.-A., Liu, W., Mei, T.: MetaSearch: incremental product search via deep meta-learning. Trans. Img. Proc. 29, 7549–7564 (2020). https://doi.org/10.1109/TIP.2020.3004249
Yang, X., He, H., Wu, Y., Tang, C., Chen, H., Liang, J.: User intent perception by gesture and eye tracking. Cogent Eng. (2016). https://doi.org/10.1080/23311916.2016.1221570
Papadopoulos, S.-I., Koutlis, C., Papadopoulos, S., Kompatsiaris, I.: Multimodal Quasi-AutoRegression: forecasting the visual popularity of new fashion products. Int. J. Multimed. Inf. Retr. (2022). https://doi.org/10.1007/s13735-022-00262-5
Chiou, R., Mookiah, P., Kwon, Y.: Manufacturing e-quality through integrated web-enabled computer vision and robotics. Int. J. Adv. Manuf. Technol. 43, 720–730 (2009). https://doi.org/10.1007/s00170-008-1747-3
Jiang, C., Yang, J., Zhang, L., Wang, X.: A high-precision hand-held face detection system. J. Multimed. 8, 256 (2013)
Song, H.: IOT-oriented visual target tracking and supply chain art product design. Mob. Inf. Syst. (2022). https://doi.org/10.1155/2022/3773469
Ziaratban, A., Azadbakht, M., Ghasemnezhad, A.: Modeling of volume and surface area of apple from their geometric characteristics and artificial neural network. Int. J. Food Prop. 20, 762–768 (2017)
James, M.R., Chandler, J.H., Eltner, A., Fraser, C., Miller, P.E., Mills, J.P., Noble, T., Robson, S., Lane, S.N.: Guidelines on the use of structure-from-motion photogrammetry in geomorphic research. Earth. Surf. Proc. Land. 44, 2081–2084 (2019)
Liu, C., Pan, Z., Zhang, C., Miao, W.: Nonheritage creative product design and development and marketing strategies for computer vision and user experience. Secur. Commun. Netw. (2022). https://doi.org/10.1155/2022/9685280
Papachristou, E., Chrysopoulos, A., Bilalis, N.: Machine learning for clothing manufacture as a mean to respond quicker and better to the demands of clothing brands: a Greek case study. Int. J. Adv. Manuf. Technol. 115, 691–702 (2021)
Llave, Y., Fukuda, S., Fukuoka, M., Shibata-Ishiwatari, N., Sakai, N.: Analysis of color changes in chicken egg yolks and whites based on degree of thermal protein denaturation during ohmic heating and water bath treatment. J. Food Eng. 222, 151–161 (2018)
Huynh, T.T., TonThat, L., Dao, S.V.: A vision-based method to estimate volume and mass of fruit/vegetable: case study of sweet potato. Int. J. Food Prop. 25, 717–732 (2022)
Pace, B., Cavallo, D.P., Cefola, M., Colella, R., Attolico, G.: Adaptive self-configuring computer vision system for quality evaluation of fresh-cut radicchio. Innov. Food Sci. Emerg. Technol. 32, 200–207 (2015)
Gupta, K., Körber, M., Djavadifar, A., Krebs, F., Najjaran, H.: Wrinkle and boundary detection of fiber products in robotic composites manufacturing. Assem. Autom. 40, 283–291 (2020)
Hwang, S., Choi, Y., Koo, S.: Shape reconstruction and inspection using multi-planar X-ray images. Int. J. Precis. Eng. Manuf. 15, 1545–1551 (2014)
Shi, Z., Ma, Y., Fu, M.: Fuzzy support tensor product adaptive image classification for the internet of things. Comput. Intell. Neurosci.. Intell. Neurosci. (2022). https://doi.org/10.1155/2022/3532605
Cao, M.W., Li, S.J., Jia, W., Liu, X.P.: A survey on feature tracking methods for SFM. Jisuanji Xuebao 41, 2536–2565 (2018). https://doi.org/10.11897/SP.J.1016.2018.02536
García-Ruiz, P., Muñoz-Salinas, R., Medina-Carnicer, R., Marín-Jiménez, M.J.: Object localization with multiplanar fiducial markers: accurate pose estimation. In: Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 454–465 (2023)
Moura, G.M., Vieira, M.B., Silva, R.L.D.S.D: VEM-SLAM: virtual environment modelling through SLAM. In: Proceedings of the Proceedings - 2020 22nd Symposium on Virtual and Augmented Reality, SVR 2020, pp. 242–251 (2020)
Wang, Q., Wang, Z., Li, B., Wei, D.: An improved YOLOv3 object detection network for mobile augmented reality. In: Proceedings of the International Conference on Virtual Rehabilitation, ICVR, pp. 332–339 (2021)
Elmagrouni, I., Ettaoufik, A., Aouad, S., Maizate, A.: A deep learning framework for hand gesture recognition and multimodal interface control. Rev. Intell. Artif. 37, 881–887 (2023). https://doi.org/10.18280/ria.370407
Haji Mohd, M.N., MohdAsaari, M.S., Lay Ping, O., Rosdi, B.A.: Vision-based hand detection and tracking using fusion of kernelized correlation filter and single-shot detection. Appl. Sci. (2023). https://doi.org/10.3390/app13137433
Zhao, R., Ge, Y., Duan, Y., Jiang, Q.: Large-field gesture tracking and recognition for augmented reality interaction. In: Proceedings of the Journal of Physics: Conference Series (2023)
Krishna, S., Shanthappa Vandrotti, B.: DeepSmooth: efficient and smooth depth completion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 3358–3367 (2023)
Esser, P., Haux, J., Milbich, T., Ommer, B.: Towards learning a realistic rendering of human behavior. In: Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 409–425 (2019)
Jiang, J., Huang, Z., Qian, W., Zhang, Y., Liu, Y.: Registration technology of augmented reality in oral medicine: a review. IEEE Access 7, 53566–53584 (2019). https://doi.org/10.1109/ACCESS.2019.2912949
Lee, D., Yi, J.W., Hong, J., Chai, Y.J., Kim, H.C., Kong, H.J.: Augmented reality to localize individual organ in surgical procedure. Healthc. Inform. Res. 24, 394–401 (2018). https://doi.org/10.4258/hir.2018.24.4.394
Elhagry, A., El Saddik, A.: Text-to-metaverse: towards a digital twin-enabled multimodal conditional generative metaverse. In: Proceedings of the Proceedings—2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023, pp. 666–669 (2023)
Jacob, R., Sugitha, N.: A review on vision based real time fingertip detection approaches. Iraqi J. Sci. 64, 4014–4022 (2023). https://doi.org/10.24996/ijs.2023.64.6.39
Rodriguez-Lozano, F.J., Gámez-Granados, J.C., Martínez, H., Palomares, J.M., Olivares, J.: 3D reconstruction system and multiobject local tracking algorithm designed for billiards. Appl. Intell. 53, 21543–21575 (2023). https://doi.org/10.1007/s10489-023-04542-3
Shamsabadi, A., Mojdeganlou, H., Barzegary, A., Fakhfouri, A., Azad, K., Heydari, M., Pashaei, Z., Mehraeen, E.: Opportunities, threats and solution techniques of deep-fake technology: a systematic review. In: Proceedings of the Proceedings of the International Conferences on e-Society 2022 and Mobile Learning 2022, pp. 19–25 (2022)
Taylor, C., Mullany, C., McNicholas, R., Cosker, D.: VR props: an end-to-end pipeline for transporting real objects into virtual and augmented environments. In: Proceedings of the Proceedings—2019 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2019, pp. 83–92 (2019)
Marelli, D., Bianco, S., Ciocca, G.: Designing an AI-Based virtual try-on web application. Sensors (2022). https://doi.org/10.3390/s22103832
Papazoglou Chalikias, A., Kouslis, E., Sarakatsanos, O., Boikou, A., Papadopoulos, S.I., Koutlis, C., Papadopoulos, S., Nikolopoulos, S., Kompatsiaris, I., Gavilan, D. et al.: Novel paradigms of human-fashion interaction. In Proceedings of the ACM International Conference Proceeding Series (2022)
You, Y., Boyer, A., Jokela, T., Piippo, P.: SelfieWall: a mixed reality advertising platform. In: Proceedings of the Adjunct Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2017, pp. 240–244 (2017)
Zhang, B.: Augmented reality virtual glasses try-on technology based on iOS platform. Eurasip J. Image Video Process. (2018). https://doi.org/10.1186/s13640-018-0373-8
Avgerinakis, K., Meditskos, G., Derdaele, J., Mille, S., Shekhawat, Y., Fraguada, L., Lopez, E., Wuyts, J., Tellios, A., Riegas, S. et al.: V4Design for enhancing architecture and video game creation. In: Proceedings of the Adjunct Proceedings—2018 IEEE International Symposium on Mixed and Augmented Reality, ISMAR-Adjunct 2018, pp. 305–309 (2018)
Bajaj, A., Rajpal, J., Abraham, A.: A survey on 3D hand detection and tracking algorithms for human computer interfacing. In: Proceedings of the Lecture Notes in Networks and Systems, pp. 384–395 (2023)
Vidya, M., Vineela, S., Sathish, P., Reddy, A.S.: Gesture-based control of presentation slides using OpenCV. In: Proceedings of the Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023, pp. 1786–1791 (2023)
Dash, A., Jena, S.P., Gantayet, A.: Administrator controlled timetable based automatic facial recognition attendance system. In : Proceedings of the 2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023, pp. 663–668 (2023)
Lawal, C.O., Akinrinmade, A.A., Badejo, J.A.: Face-based gender recognition analysis for Nigerians using CNN. In: Proceedings of the Journal of Physics: Conference Series (2019)
Mai, N.T.L., Ahmad Ridzuan, S.S.B., Omar, Z.B.: Content-based image retrieval system for an image gallery search application. Int. J. Electr. Comput. Eng. 8, 1903–1912 (2018). https://doi.org/10.11591/ijece.v8i3.pp1903-1912
Berčík, J., Mravcová, A., Gálová, J., Jadroňová, S.: The use of computer vision and data mining in obtaining subconscious user experience. In: Proceedings of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022 (2022)
Konin, A., Siddiqui, S., Gilani, H., Mudassir, M., Ahmed, M.H., Shaukat, T., Naufil, M., Ahmed, A., Tran, Q.H., Zeeshan Zia, M.: AI-mediated job status tracking in AR as a no-code service. In: Proceedings of the Proceedings - 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2022, pp. 915–916 (2022)
Lee, S., Aldas, N.D.T., Lee, C., Rosson, M.B., Carroll, J.M., Narayanan, V.: AIGuide: augmented reality hand guidance in a visual prosthetic. ACM Trans. Access. Comput. (2022). https://doi.org/10.1145/3508501
Bakaev, M., Heil, S., Khvorostov, V., Gaedke, M.: Auto-extraction and integration of metrics for web user interfaces. J. Web Eng. 17, 561–590 (2018). https://doi.org/10.13052/jwe1540-9589.17676
Bakaev, M., Heil, S., Khvorostov, V., Gaedke, M.: HCI vision for automated analysis and mining of web user interfaces. In: Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 136–144 (2018)
Iliev, Y., Ilieva, G.: A framework for smart home system with voice control using NLP methods. Electronics (Switzerland) (2023). https://doi.org/10.3390/electronics12010116
Malcangi, M.: Smart recognition and synthesis of emotional speech for embedded systems with natural user interfaces. In: Proceedings of the Proceedings of the International Joint Conference on Neural Networks, pp. 867–871 (2011)
Sharma, A., Ahmed, V., Sharma, S., Jana, B., Rani, K.: An effective approach to speech-based email assistance for visually impaired people. In: Proceedings of the 2022 8th International Conference on Signal Processing and Communication, ICSC 2022, pp. 32–35 (2022)
Prihodova, K., Hub, M.: Hand-based biometric system using convolutional neural networks. Acta Inform. Prag. 9, 48–57 (2020). https://doi.org/10.18267/j.aip.131
Qazi, F.A.: A survey of biometric authentication systems. In: Proceedings of the Proceedings of the International Conference on Security and Management, SAM'04, pp. 61–67 (2004)
Huang, X., Zhou, Y., Du, Y.: A novel bi-dual inference approach for detecting six-element emotions. Appl. Sci. (2023). https://doi.org/10.3390/app13179957
Kumari, R., Wasim, J.: A deep learning approach for human facial expression recognition using residual network – 101. J. Curr. Sci. Tech. 13, 517–532 (2023). https://doi.org/10.59796/jcst.V13N3.2023.2152
Wang, J.Z., Zhao, S., Wu, C., Adams, R.B., Newman, M.G., Shafir, T., Tsachor, R.: Unlocking the emotional world of visual media: an overview of the science, research, and impact of understanding emotion. Proc. IEEE 111, 1236–1286 (2023). https://doi.org/10.1109/JPROC.2023.3273517
Waelen, R.A.: The ethics of computer vision: an overview in terms of power. AI Eth. (2023). https://doi.org/10.1007/s43681-023-00272-x
Dhirani, L.L., Mukhtiar, N., Chowdhry, B.S., Newe, T.: Ethical dilemmas and privacy issues in emerging technologies: a review. Sensors (2023). https://doi.org/10.3390/s23031151
Mehmood, K., Qiu, X., Abrar, M.M.: Unearthing research trends in emissions and sustainable development: potential implications for future directions. Gondwana Res. Res. 119, 227–245 (2023). https://doi.org/10.1016/j.gr.2023.02.009
Bilal, M., Harasani, W.I., Yang, L.: Rapid prototyping of image contrast enhancement hardware accelerator on FPGAs Using high-level synthesis tools. Jordan. J. Electr. Eng. 9, 322–337 (2023). https://doi.org/10.5455/jjee.204-1673105856
Spasennikov, V., Androsov, K., Golubeva, G.: Ergonomic factors in patenting computer systems for personnel's selection and training. In: Proceedings of the CEUR Workshop Proceedings (2020)
Zhang, J., Chen, D., Liao, J., Zhang, W., Feng, H., Hua, G., Yu, N.: Deep model intellectual property protection via deep watermarking. IEEE Trans. Patt. Anal. Mach. Intell. 44, 4005–4020 (2022). https://doi.org/10.1109/TPAMI.2021.3064850
Bloom, G., Alsulami, B., Nwafor, E., Bertolotti, I.C.: Design patterns for the industrial Internet of Things. In: Proceedings of the IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS, pp. 1–10 (2018)
Wazzan, W.: Updating the law of trade secrets in Saudi Arabia. Indones. J. Intl. Comp. Law 5, 43–73 (2018)
Filicori, F., Addison, P.: Intellectual property and data ownership in the age of video recording in the operating room. Surg. Endosc. 36, 3772–3774 (2022). https://doi.org/10.1007/s00464-021-08692-8
Aristodemou, L., Tietze, F.: The state-of-the-art on Intellectual property analytics (IPA): a literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Inf. 55, 37–51 (2018). https://doi.org/10.1016/j.wpi.2018.07.002
Prihastomo, Y., Kosala, R., Supangkat, S.H., Ranti, B., Trisetyarso, A.: Theoretical framework of smart intellectual property office in developing countries. Proc. Comput. Sci. 161, 994–1001 (2019). https://doi.org/10.1016/j.procs.2019.11.209
Nemlioglu, I.: A comparative analysis of intellectual property rights: a case of developed versus developing countries. Proc. Comput. Sci. 158, 988–998 (2019). https://doi.org/10.1016/j.procs.2019.09.140
Xie, Z., Ouyang, X., Liu, X., Xing, G.: UltraDepth: Exposing high-resolution texture from depth cameras. In: Proceedings of the SenSys 2021—Proceedings of the 2021 19th ACM Conference on Embedded Networked Sensor Systems, pp. 302–315 (2021)
Li, Z., Ma, L., Chen, M., Xiao, J., Gu, Q.: Patch similarity aware data-free quantization for vision transformers. In: Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 154–170 (2022)
Wang, Z., Yang, G., Dai, H., Rong, C.: Privacy-preserving split learning for large-scaled vision pre-training. IEEE Trans. Inf. Forensics Secur. 18, 1539–1553 (2023). https://doi.org/10.1109/TIFS.2023.3243490
Battiston, I.: Improving Data minimization through decentralized data architectures. In: Proceedings of the CEUR Workshop Proceedings, pp. 25–28 (2023)
Miri Rostami, S., Samet, S., Kobti, Z.: A study of blockchain-based federated learning. In: Adaptation, Learning, and Optimization, Volume 27, pp. 139–165, Springer Science and Business Media Deutschland GmbH (2023)
Lai, C., Wang, Y., Wang, H., Zheng, D.: A blockchain-based traceability system with efficient search and query. Peer-to-Peer Netw. Appl. 16, 675–689 (2023). https://doi.org/10.1007/s12083-022-01438-w
Shaikh, T.A., Rasool, T., Verma, P.: Machine intelligence and medical cyber-physical system architectures for smart healthcare: taxonomy, challenges, opportunities, and possible solutions. Artif. Intell. Med.. Intell. Med. (2023). https://doi.org/10.1016/j.artmed.2023.102692
Coelho, K.K., Tristão, E.T., Nogueira, M., Vieira, A.B., Nacif, J.A.M.: Multimodal biometric authentication method by federated learning. Biomed. Signal Process. Control (2023). https://doi.org/10.1016/j.bspc.2023.105022
Liu, F., Liu, H., Kannadasan, R., Jiang, Q.: A biometric-based implicit authentication protocol with privacy protection for ubiquitous communication environments. Int. J. Commun. Syst. (2023). https://doi.org/10.1002/dac.5578
Sharma, S.B., Dhall, I., Nayak, S.R., Chatterjee, P.: Reliable biometric authentication with privacy protection. In: Proceedings of the Lecture Notes in Electrical Engineering, pp. 233–249 (2023)
Herman, S.: Artificial intelligence, machine learning, and computer vision. In: SMART MANUFACTURING: The Lean Six Sigma Way, pp. 205–217, Wiley (2022)
Kasianchuk, A., Honcharenko, Y., Popovych, N., Nechyporuk, R., Nechyporuk, Y.: The future of artificial intelligence, computer vision, and machine learning and its potential to shape the way we live and interact with technology. In: Proceedings of the SIST 2023—2023 IEEE International Conference on Smart Information Systems and Technologies, Proceedings, pp. 72–78 (2023)
Scheuerman, M.K., Weathington, K., Mugunthan, T., Denton, E., Fiesler, C.: From human to data to dataset: mapping the traceability of human subjects in computer vision datasets. Proc. ACM Hum. Comput. Interact. (2023). https://doi.org/10.1145/3579488
Li, X., Yan, L., Zhao, L., Martinez-Maldonado, R., Gasevic, D.: CVPE: a computer vision approach for scalable and privacy-preserving socio-spatial, multimodal learning analytics. In: Proceedings of the ACM International Conference Proceeding Series, pp. 175–185 (2023)
Ly Duc, M., Hlavaty, L., Bilik, P., Martinek, R.: Enhancing manufacturing excellence with Lean Six Sigma and zero defects based on Industry 4.0. Adv. Prod. Eng. Manag. 18, 32–48 (2023). https://doi.org/10.14743/APEM2023.1.455
Yin, Z., Li, Z., Li, H.: Application of internet of things data processing based on machine learning in community sports detection. Prev. Med. (2023). https://doi.org/10.1016/j.ypmed.2023.107603
Gorlin, R.: Perspective on invasive cardiology: the 24th Louis F. Bishop lecture. J. Am. Coll. Cardiol. 23, 525–532 (1994). https://doi.org/10.1016/0735-1097(94)90442-1
Laguette, M.J.N., Suijkerbuijk, M.A.M., September, A.V.: Epigenetic regulation and musculoskeletal injuries. In: Epigenetics of Exercise and Sports: Concepts, Methods, and Current Research, pp. 235–246, Elsevier (2021)
Bricout, J., Baker, P.M.A., Moon, N.W., Sharma, B.: Exploring the smart future of participation: community, inclusivity, and people with disabilities. Intl. J. E Plan. Res. 10, 94–108 (2021). https://doi.org/10.4018/IJEPR.20210401.oa8
Shiggins, C., Coe, D., Gilbert, L., Research Collaboration, A., Mares, K.: Development of an “Aphasia-accessible participant in research experience survey” through co-production. Aphasiology (2022). https://doi.org/10.1080/02687038.2021.1996532
Wang, Q., Zhang, Z., Wang, Y., Jin, H., Sun, Q., Song, Y., Yu, H., Xue, H., Che, C.: Analysis and improvement of repeated wake-up jittering H-line. In: Proceedings of the Proceedings of the International Symposium on the Physical and Failure Analysis of Integrated Circuits, IPFA (2021)
Mefenza, M., Yonga, F., Saldanha, L.B., Bobda, C., Velipassalar, S.: A framework for rapid prototyping of embedded vision applications. In: Proceedings of the Conference on Design and Architectures for Signal and Image Processing, DASIP (2015)
Papachristou, E., Kalaitzi, D., Kaseris, M.: An innovative platform for designing and rapid virtual prototyping of garments: The case of i-mannequin. In: Proceedings of the Lecture Notes in Mechanical Engineering, pp. 354–362 (2024)
Carbone, C.: The kit of parts as medium and message for developing post-war dwellings. Hist. Postwar. Archit. 4, 54–74 (2019). https://doi.org/10.6092/issn.2611-0075/9648
Kovilpillai, J.J.A., Jayanthy, S.: An optimized deep learning approach to detect and classify defective tiles in production line for efficient industrial quality control. Neural Comput. Appl. 35, 11089–11108 (2023). https://doi.org/10.1007/s00521-023-08283-9
Lins, R.G., Santos, R.E., Gaspar, R.: Vision-based measurement for quality control inspection in the context of Industry 4.0: a comprehensive review and design challenges. J. Braz. Soc. Mech. Sci. Eng. (2023). https://doi.org/10.1007/s40430-023-04050-y
Castañé, G., Dolgui, A., Kousi, N., Meyers, B., Thevenin, S., Vyhmeister, E., Östberg, P.O.: The ASSISTANT project: AI for high level decisions in manufacturing. Int. J. Prod. Res. 61, 2288–2306 (2023). https://doi.org/10.1080/00207543.2022.2069525
Guo, Y., Ren, J., Liang, Y., Ding, Y.: Construction of digital twin for clamped near-net-shape blade in adaptive manufacturing. J. Manuf. Processes 108, 12–25 (2023). https://doi.org/10.1016/j.jmapro.2023.10.055
Sonnleithner, L., Hager, A.L., Zoitl, A., Meixner, K.: IEC 61499 skill-based distributed design pattern. In: Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, ETFA (2023)
Saenz de Ugarte, B., Jbaida, K., Artiba, A., Pellerin, R.: Adaptive manufacturing: a real-time simulation-based control system. In: Proceedings of the 2006 International Conference on Software Engineering Advances, ICSEA'06, pp. 71–77 (2006)
Comari, S., Di Leva, R., Carricato, M., Badini, S., Carapia, A., Collepalumbo, G., Gentili, A., Mazzotti, C., Staglianò, K., Rea, D.: Mobile cobots for autonomous raw-material feeding of automatic packaging machines. J. Manuf. Syst. 64, 211–224 (2022). https://doi.org/10.1016/j.jmsy.2022.06.007
Navas-Reascos, G.E., Romero, D., Rodriguez, C.A., Guedea, F., Stahre, J.: Wire harness assembly process supported by a collaborative robot: a case study focus on ergonomics. Robotics (2022). https://doi.org/10.3390/robotics11060131
Sharma, A.: Making electric vehicle batteries safer through better inspection using artificial intelligence and cobots. Int. J. Prod. Res. (2023). https://doi.org/10.1080/00207543.2023.2180308
Gordan, M., Dancea, O., Vlaicu, A., Stoian, I., Tsatos, O.: Computer vision based decision support tool for hydro-dams surface deterioration assessment and visualization using fuzzy sets and pseudo-coloring. In: Proceedings of the 2008 IEEE International Conference on Automation, Quality and Testing, Robotics, AQTR 2008: THETA 16th Edition—Proceedings, pp. 207–212 (2008)
Inselberg, A.: Visualizing high dimensional datasets and multivariate relations alfred inselberg (Tel Aviv university),visualizing high dimensional datasets & multivariate relations. In: Proceedings of the Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pp. 33–94 (2000)
Ahmed, S., Kalsoom, T., Ramzan, N., Pervez, Z., Azmat, M., Zeb, B., Ur Rehman, M.: Towards supply chain visibility using internet of things: a dyadic analysis review. Sensors (2021). https://doi.org/10.3390/s21124158
Al-Khatib, A.W.: The impact of industrial Internet of things on sustainable performance: the indirect effect of supply chain visibility. Bus. Process Manage. J. 29, 1607–1629 (2023). https://doi.org/10.1108/BPMJ-03-2023-0198
Panigrahi, R.R., Shrivastava, A.K., Qureshi, K.M., Mewada, B.G., Alghamdi, S.Y., Almakayeel, N., Almuflih, A.S., Qureshi, M.R.N.: AI chatbot adoption in SMEs for sustainable manufacturing supply chain performance: a mediational research in an emerging country. Sustainability (2023). https://doi.org/10.3390/su151813743
Hong, T., Kolios, A.: A framework for risk management of large-scale organisation supply chains. In: Proceedings of the 2020 international conference on decision aid sciences and application, DASA 2020, pp. 948–953 (2020)
Raju, K., Ravichandran, S., Khadri, S.P.M.S. Blockchain for on-demand small launch vehicle supply chain. In: Proceedings of the Proceedings of the International Astronautical Congress, IAC (2018)
Yao, Y., Saccomandi, P., Tarabini, M.: User-driven design and monitoring systems of limb prostheses: overview on the technology and on the gender-related aspects. In: Proceedings of the 2021 IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2021—Proceedings, pp. 313–318 (2021)
Becue, A., Fourastier, Y., Praca, I., Savarit, A., Baron, C., Gradussofs, B., Pouille, E., Thomas, C.: CyberFactory#1: securing the industry 4.0 with cyber-ranges and digital twins. In: Proceedings of the IEEE International Workshop on Factory Communication Systems—Proceedings, WFCS, pp. 1–4 (2018)
Terzimehic, T., Wenger, M., Zoitl, A., Bayha, A., Becker, K., Müller, T., Schauerte, H.: Towards an industry 4.0 compliant control software architecture Using IEC 61499 & OPC UA. In: Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, pp. 1–4 (2017)
Villalba-Diez, J., Schmidt, D., Gevers, R., Ordieres-Meré, J., Buchwitz, M., Wellbrock, W.: Deep learning for industrial computer vision quality control in the printing industry 4.0. Sensors (2019). https://doi.org/10.3390/s19183987
Nagy, M., Lăzăroiu, G.: Computer vision algorithms, remote sensing data fusion techniques, and mapping and navigation tools in the industry 4.0-based Slovak automotive sector. Mathematics (2022). https://doi.org/10.3390/math10193543
Penumuru, D.P., Muthuswamy, S., Karumbu, P.: Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. J. Intell. Manuf.Intell. Manuf. 31, 1229–1241 (2020). https://doi.org/10.1007/s10845-019-01508-6
Pransky, J.: The Pransky interview: Mitchell Weiss, CTO, seegrid corporation. Ind. Robot. 44, 137–141 (2017). https://doi.org/10.1108/IR-01-2017-0012
Laucka, A., Adaskeviciute, V., Valinevicius, A., Andriukaitis, D.: Research of the equipment calibration methods for fertilizers particles distribution by size using image processing measurement method. In: Proceedings of the 2018 23rd International Conference on Methods and Models in Automation and Robotics, MMAR 2018, pp. 407–412 (2018)
Benbarrad, T., Salhaoui, M., Kenitar, S.B., Arioua, M.: Intelligent machine vision model for defective product inspection based on machine learning. J. Sens. Actuator Netw. 10, 7 (2021)
Güldenring, R., van Evert, F.K., Nalpantidis, L.: RumexWeeds: a grassland dataset for agricultural robotics. J. Field. Rob. 40, 1639–1656 (2023). https://doi.org/10.1002/rob.22196
Kim, D., Choi, M., Um, J.: Digital twin for autonomous collaborative robot by using synthetic data and reinforcement learning. Rob. Comput. Integr. Manuf. (2024). https://doi.org/10.1016/j.rcim.2023.102632
Anjum, M.U., Khan, U.S., Qureshi, W.S., Hamza, A., Khan, W.A.: Vision-based hybrid detection for pick and place application in robotic manipulators. In: Proceedings of the 2023 International Conference on Robotics and Automation in Industry, ICRAI 2023 (2023)
Mustafin, M., Chebotareva, E., Li, H., Martínez-García, E.A., Magid, E.: Features of interaction between a human and a gestures-controlled collaborative robot in an assembly task: pilot experiments. In: Proceedings of the Proceedings of International Conference on Artificial Life and Robotics, pp. 158–162 (2023)
Maitre, J., Rendu, C., Bouchard, K., Bouchard, B., Gaboury, S.: Object recognition in performed basic daily activities with a handcrafted data glove prototype. Pattern Recogn. Lett. 147, 181–188 (2021). https://doi.org/10.1016/j.patrec.2021.04.017
Zhao, G., Ma, H., Jin, Y.: A Method for robust object recognition and pose estimation of rigid body based on point cloud. In: Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 468–480 (2022)
Lindner, T., Wyrwał, D., Milecki, A.: An autonomous humanoid robot designed to assist a human with a gesture recognition system. Electronics 12, 2652 (2023)
Choksi, S., Szot, S., Zang, C., Yarali, K., Cao, Y., Ahmad, F., Xiang, Z., Bitner, D.P., Kostic, Z., Filicori, F.: Bringing Artificial Intelligence to the operating room: edge computing for real-time surgical phase recognition. Surg. Endosc. 37, 8778–8784 (2023). https://doi.org/10.1007/s00464-023-10322-4
Li, C., Chen, H.: Cultural psychology of english translation through computer vision-based robotic interpretation. Learn. Motiv.Motiv. (2023). https://doi.org/10.1016/j.lmot.2023.101938
Lu, J., Gong, P., Ye, J., Zhang, J., Zhang, C.: A survey on machine learning from few samples. Patt. Recogn. (2023). https://doi.org/10.1016/j.patcog.2023.109480
Furman, J.L., Teodoridis, F.: Automation, research technology, and researchers’ trajectories: evidence from computer science and electrical engineering. Organ. Sci. 31, 330–354 (2020). https://doi.org/10.1287/orsc.2019.1308
Paneru, S., Jeelani, I.: Computer vision applications in construction: current state, opportunities & challenges. Autom. Constr. 132, 103940 (2021). https://doi.org/10.1016/j.autcon.2021.103940
Jiang, W., Kumar, V., Mehta, N., Bott, J., Modi, V.: Irrigation detection by car: computer vision and sensing for the detection and geolocation of irrigated and non-irrigated farmland. In: Proceedings of the 2020 IEEE Global Humanitarian Technology Conference, GHTC 2020 (2020)
Placidi, G., De Gasperis, G., Mignosi, F., Polsinelli, M., Spezialetti, M.: Integration of a BCI with a hand tracking system and a motorized robotic arm to improve decoding of brain signals related to hand and finger movements. In: Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 305–315 (2021)
Wong, A., Famuori, M., Shafiee, M.J., Li, F., Chwyl, B., Chung, J.: YOLO nano: a highly compact you only look once convolutional neural network for object detection. In: Proceedings of the Proceedings—5th Workshop on Energy Efficient Machine Learning and Cognitive Computing, EMC2-NIPS 2019, pp. 22–25 (2019)
Huang, R., Huang, Z., Su, S.: A Faster, lighter and stronger deep learning-based approach for place recognition. In: Proceedings of the Communications in Computer and Information Science, pp. 453–463 (2023)
Zhang, Y.: Application of wireless sensor network integrated with 3–5g Technology in the design of interactive space in an urban landscape. Endor. Trans. Scalable. Inf. Syst. 10, 1–12 (2023). https://doi.org/10.4108/eetsis.v10i3.3063
Musharaf Hussain, M.M., Rahman, M.M., Uddin, M.S., Arefin, M.S.: IoT based smart human traffic monitoring system using raspberry Pi. In: Proceedings of the Lecture Notes in Networks and Systems, pp. 361–372 (2023)
Thanki, R., Joshi, P.: Advanced Technologies for Industrial Applications, pp. 1–98. Springer International Publishing (2023)
Deshpande, S., Padalkar, S., Anand, S.: IIoT based framework for data communication and prediction using augmented reality for legacy machine artifacts. Manuf. Let. 35, 1043–1051 (2023). https://doi.org/10.1016/j.mfglet.2023.08.058
Hashmi, M.F., Keskar, A.G.: Machine Learning and Deep Learning for Smart Agriculture and Applications, pp. 1–257. IGI Global (2023)
Klinaku, V., Qatipi, A., Turku, D., Zhubi, L.: Design for mobile view website using model view controller. In: Designing and Developing Innovative Mobile Applications, pp. 267–281, IGI Global (2023)
Naderi, H., Shojaei, A., Ly, R.: Autonomous construction safety incentive mechanism using blockchain-enabled tokens and vision-based techniques. Autom. Constr. Constr (2023). https://doi.org/10.1016/j.autcon.2023.104959
Ramalingam, M., Selvi, G.C., Victor, N., Chengoden, R., Bhattacharya, S., Maddikunta, P.K.R., Lee, D., Piran, M.J., Khare, N., Yenduri, G., et al.: A comprehensive analysis of blockchain applications for securing computer vision systems. IEEE Access 11, 107309–107330 (2023). https://doi.org/10.1109/ACCESS.2023.3319089
Kapoor, V., Naik, P.: Augmented reality-enabled education for middle schools. SN Comput. Sci. (2020). https://doi.org/10.1007/s42979-020-00155-6
Wei, G., Piyan, L., Hai, Z., Zhan, M.: The research and application of image recognition based on improved BP algorithm. In: Proceedings of the Proceedings—3rd International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2010, pp. 80–83 (2010)
Giro, R., Hsu, H., Kishimoto, A., Hama, T., Neumann, R.F., Luan, B., Takeda, S., Hamada, L., Steiner, M.B.: AI powered, automated discovery of polymer membranes for carbon capture. npj Comput. Mater. (2023). https://doi.org/10.1038/s41524-023-01088-3
Van Bossuyt, D.L., Hale, B., Arlitt, R., Papakonstantinou, N.: Zero-trust for the system design lifecycle. J. Comput. Inf. Sci. Eng.Comput. Inf. Sci. Eng. (2023). https://doi.org/10.1115/1.4062597
Yazici, S.: A machine-learning model driven by geometry, material and structural performance data in architectural design process. In: Proceedings of the Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe, pp. 411–418 (2020)
Alimuzzaman, S.M., Jahan, M.P.: Composite based additive manufacturing. In: Materials Horizons: From Nature to Nanomaterials, Volume Part F1488, pp. 117–151, Springer Nature (2024)
Narsimhachary, D., Kalyan Phani, M.: Additive manufacturing: environmental impact, and future perspective. In: Materials Horizons: From Nature to Nanomaterials, Volume Part F1488, pp. 295–308, Springer Nature (2024)
Vanali, M., Pavoni, S., Lanthaler, A.H., Vescovi, D.: Improving dynamic characteristics of strain gauge load cells using additive manufacturing. In: Proceedings of the Conference Proceedings of the Society for Experimental Mechanics Series, pp. 163–172 (2024)
Kokhanov, A., Prokopovich, I., Sikach, T., Dyadyura, I., Karabegovich, I.: Standardization of scanning protocols and measurements for additive manufacturing quality assurance. In: Proceedings of the Lecture Notes in Mechanical Engineering, pp. 359–368 (2024)
Park, M., Venter, M.P., Plessis, A.D.: A lattice structure coupon sample for build quality control in metal additive manufacturing. Mater. Des. (2023). https://doi.org/10.1016/j.matdes.2023.112436
Yang, Y., Kan, C.: Recurrence network-based 3D geometry representation learning for quality control in additive manufacturing of metamaterials. J. Manuf. Sci. Eng. (2023). https://doi.org/10.1115/1.4063236
Barrett, T., Chen, Q., Zhang, A.: Skin deep: Investigating subjectivity in skin tone annotations for computer vision benchmark datasets. In: Proceedings of the ACM International Conference Proceeding Series, pp. 1757–1771 (2023)
Liu, X., Nicolau, J.L., Law, R., Li, C.: Applying image recognition techniques to visual information mining in hospitality and tourism. Int. J. Contemp. Hosp. Manage. 35, 2005–2016 (2023). https://doi.org/10.1108/IJCHM-03-2022-0362
Yang, X., Jia, X., Gong, D., Yan, D.M., Li, Z., Liu, W.: LARNeXt: End-to-end lie algebra residual network for face recognition. IEEE Trans. Patt. Anal. Mach. Intell. 45, 11961–11976 (2023). https://doi.org/10.1109/TPAMI.2023.3279378
Aikyn, N., Zhanegizov, A., Aidarov, T., Bui, D.M., Tu, N.A.: Efficient facial expression recognition framework based on edge computing. J. Supercomput. (2023). https://doi.org/10.1007/s11227-023-05548-x
Xue, P., Wang, C., Huang, W., Jiang, G., Zhou, G., Raza, M.: Pupil centre’s localization with transformer without real pupil. Multimed. Tools Appl 82, 25467–25484 (2023). https://doi.org/10.1007/s11042-023-14403-3
Jaiswal, D., Kumar, P.: A survey on parallel computing for traditional computer vision. Concurr. Comput. Pract. Exper. (2022). https://doi.org/10.1002/cpe.6638
Koubaa, A., Ammar, A., Alahdab, M., Kanhouch, A., Azar, A.T.: Deepbrain: experimental evaluation of cloud-based computation offloadingand edge computing in the internet-of-drones for deep learning applications. Sensors (Switzerland) 20, 1–25 (2020). https://doi.org/10.3390/s20185240
Li, C., Zhang, C., Shi, L., Zheng, R., Shen, Q.: Hard disk posture recognition and grasping based on depth vision. In Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 539–550 (2023)
Prasad, A.O., Mishra, P., Jain, U., Pandey, A., Sinha, A., Yadav, A.S., Kumar, R., Sharma, A., Kumar, G., Hazim Salem, K., et al.: Design and development of software stack of an autonomous vehicle using robot operating system. Rob. Autom. Syst. (2023). https://doi.org/10.1016/j.robot.2022.104340
Wang, G., Wu, X., Xin, B., Gu, X., Wang, G., Zhang, Y., Zhao, J., Cheng, X., Chen, C., Ma, J.: Machine learning in unmanned systems for chemical synthesis. Molecules (2023). https://doi.org/10.3390/molecules28052232
Ferreira, L., Malkowsky, S., Persson, P., Karlsson, S., Åström, K., Liu, L.: Design of an application-specific VLIW vector processor for ORB feature extraction. J. Signal Process Syst. 95, 863–875 (2023). https://doi.org/10.1007/s11265-022-01833-9
Shen, H., Petrossian, A., Vizcarra, J.A., Schiorring, E., Tufenkjian, M.: Robotics-empowered convergence engineering education. In: Proceedings of the ASEE Annual Conference and Exposition, Conference Proceedings (2023)
Benbarrad, T., Salhaoui, M., Kenitar, S.B., Arioua, M.: Intelligent machine vision model for defective product inspection based on machine learning. J. Sens. Actuator Netw.Netw. (2021). https://doi.org/10.3390/jsan10010007
Wang, J., Fu, P., Gao, R.X.: Machine vision intelligence for product defect inspection based on deep learning and Hough transform. J. Manuf. Syst. 51, 52–60 (2019). https://doi.org/10.1016/j.jmsy.2019.03.002
Bi, Z.M., Luo, C., Miao, Z., Zhang, B., Zhang, W.J., Wang, L.: Safety assurance mechanisms of collaborative robotic systems in manufacturing. Rob. Comput. Integr. Manuf. 67, 102022 (2021). https://doi.org/10.1016/j.rcim.2020.102022
García-Esteban, J.A., Piardi, L., Leitão, P., Curto, B., Moreno, V.: An interaction strategy for safe human co-working with industrial collaborative robots. In: Proceedings of the 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), pp. 585–590, 10–12 May 2021 (2021)
Medina, A.C., Mora, J.F., Martinez, C., Barrero, N., Hernandez, W.: Safety protocol for collaborative human-robot recycling tasks. IFAC-PapersOnLine 52, 2008–2013 (2019). https://doi.org/10.1016/j.ifacol.2019.11.498
Sardar, P., Abbott, J.D., Kundu, A., Aronow, H.D., Granada, J.F., Giri, J.: Impact of artificial intelligence on interventional cardiology. JACC Cardiovasc. Interv.Cardiovasc. Interv. 12, 1293–1303 (2019). https://doi.org/10.1016/j.jcin.2019.04.048
Pajer, S., Streit, M., Torsney-Weir, T., Spechtenhauser, F., Möller, T., Piringer, H.: WeightLifter: visual weight space exploration for multi-criteria decision making. IEEE Trans. Visual Comput. Graph. 23, 611–620 (2017). https://doi.org/10.1109/TVCG.2016.2598589
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Yang, L., Kumar, R., Kaur, R. et al. Exploring the role of computer vision in product design and development: a comprehensive review. Int J Interact Des Manuf 18, 3633–3680 (2024). https://doi.org/10.1007/s12008-024-01765-7
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DOI: https://doi.org/10.1007/s12008-024-01765-7