A Study on Data Science in Computer Vision Trends and Future Emerging Technologies
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V1I4P101Keywords:
Computer Vision, Human Computer Interface (HCI), Brain Computer Interface (BCI), Data Analysis, Generative Adversarial Networks (GAN)Abstract
This paper explores the rapidly developing field of data science as it relates to computer vision, with an emphasis on present developments and future directions for emerging technologies. We investigate the dynamic environment produced by developments in deep learning, specifically Convolutional Neural Networks (CNNs), and their applications in various computer vision tasks by undertaking an extensive survey of current research. Furthermore, we explore the role of interpretability and explainability approaches, few-shot and zero-shot learning strategies, and the combination of edge computing and Internet of Things (IoT) devices with computer vision. This paper analyzes the futuristic view on computer vision with data science focusing on the technology that is emerging. Additionally covered are ethical issues in the design and implementation of computer vision systems. in the field of computer vision, this study offers a thorough review of the state and future potential of data science in the field
References
[1] The Role of Computer Vision in Data Science, 2023. https://iabac.org/blog/the-role-of-computer-vision-in-data-science#:~:text=Data%20science%2C%20in%20turn%2C%20enhances,features%20within%20images%20and%20videos.
[2] Computer Vision Trends – The Ultimate 2024 Overview. Read more at: https://viso.ai/computer-vision/computer-vision-trends/
[3] https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-computer-vision/
[4] How does the computer vision work? https://freecontent.manning.com/mental-model-graphic-grokking-deep-learning-for-computer-vision/
[5] Recent Advances in Computer Vision. https://link.springer.com/book/10.1007/978-3-030-03000-1
[6] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition.
[7] osinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems.
[8] Konečnỳ, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016). Federated optimization: Distributed optimization beyond the datacenter. arXiv preprint arXiv:1511.03575.
[9] The Future of Computer Vision: Trends, Applications, & Impacts in 2023 (augmentedstartups.com)
[10] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems (NIPS) (pp. 1097-1105).
[11] He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961-2969. doi:10.1109/ICCV.2017.322
[12] Computer Vision in Surveillance and Security: A New Eye on Safety, https://www.linkedin.com/pulse/title-computer-vision-surveillance-security-new-eye-balamurugan
[13] Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Advances in Neural Information Processing Systems (NeurIPS) (pp. 13-23).
[14] Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT) (pp. 77-91).
[15] Azuma, R. T. (1997). A survey of augmented reality. Presence: Teleoperators & Virtual Environments, 6(4), 355-385.
[16] Li, Y., Zhang, L., & Sun, Y. (2019). Urban Traffic Flow Prediction Using Deep Spatio-Temporal Residual Networks. IEEE Transactions on Intelligent Transportation Systems, 20(2), 526-535.
[17] Colombo, A., del Pobil, A. P., Melchiorri, C., Siciliano, B., & Khatib, O. (2014). Handbook of Robotics. Springer International Publishing. https://doi.org/10.1007/978-3-319-08338-4
[18] Chen, H., Seow, M. L., & Leung, H. (2019). Deep Learning for Object Detection: A Comprehensive Review. arXiv preprint arXiv:1907.09408. https://arxiv.org/abs/1907.09408
[19] Yadav, A., & Prasad, R. (2019). Applications of computer vision in retail. In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 47–52). IEEE. https://doi.org/10.1109/SPIN.2019.8711744
[20] Srinivasan, V., & Shridhar, M. (2019). Machine Vision and its Applications in Manufacturing: A Review. In 2019 International Conference on Automation, Computational and Technology Management (ICACTM) (pp. 1-6). IEEE.
[21] Norman, D. A. (2013). The design of everyday things: Revised and expanded edition. Basic Books.
[22] Lebedev, M. A., & Nicolelis, M. A. L. (2006). Brain–machine interfaces: past, present and future. Trends in Neurosciences, 29(9), 536–546.
[23] Brain Computer Interface Technology. https://insights2techinfo.com/brain-computer-interface-technology/
[24] Shizheng Zhou, Computer vision meets microfluidics: a label-free method for high-throughput cell analysis
[25] Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms https://arxiv.org/abs/2312.12872
[26] https://www.hindawi.com/journals/cin/2018/7068349/ Deep Learning for Computer Vision: A Brief Review
[27] Top 5 computer vision trends in 2023. https://www.aiacceleratorinstitute.com/top-5-computer-vision-trends-in-2023/
[28] Robotic Handling of Surgical Instruments in a Cluttered Tray. http://dx.doi.org/10.1109/TASE.2015.2396041
[29] Goodfellow, I., et al. (2014). Generative adversarial nets. In Advances in neural information processing systems.
[30] Gatys, L. A., et al. (2016). Image style transfer using convolutional neural networks. In CVPR.
[31] Han, S., et al. (2015). Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. In ICLR.
[32] Hazirbas, C., et al. (2016). Fusionnet: A deep fully residual convolutional neural network for image segmentation in connectomics. In MICCAI.
[33] Isola, P., et al. (2017). Image-to-image translation with conditional adversarial networks. In CVPR.
[34] Jobin, A., et al. (2019). Artificial intelligence: The global landscape of ethics guidelines. In Nature Machine Intelligence.
[35] Lake, B. M., et al. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences.
[36] LeCun, Y., et al. (2015). Deep learning. Nature.
[37] Madry, A., et al. (2018). Towards deep learning models resistant to adversarial attacks. In ICLR.
[38] Papernot, N., et al. (2016). Distillation as a defense to adversarial perturbations against deep neural networks. In S&P.
[39] Qi, C. R., et al. (2017). Pointnet: Deep learning on point sets for 3D classification and segmentation. In CVPR.
[40] Ribeiro, M. T., et al. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In ACM SIGKDD.
[41] Schonfeld, E., et al. (2019). Generalized zero-and few-shot learning via aligned variational autoencoders. In NeurIPS.
[42] Shi, W., et al. (2016). Edge computing: Vision and challenges. In IEEE Internet of Things Journal.
[43] Simonyan, K., et al. (2014). Deep inside convolutional networks: Visualising image classification models and saliency maps. In arXiv preprint arXiv:1312.6034.
[44] Srivastava, N., et al. (2012). Multimodal learning with deep Boltzmann machines. In NeurIPS.
[45] Szegedy, C., et al. (2013). Intriguing properties of neural networks. In ICLR.
[46] Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In ICML.
[47] Tramer, F., et al. (2016). Stealing machine learning models via prediction APIs. In USENIX Security.
[48] Vaswani, A., et al. (2017). Attention is all you need. In NeurIPS.
[49] Wang, Y. X., et al. (2019). Generalizing from a few examples: A survey on few-shot learning. arXiv preprint arXiv:1904.05046.
[50] Wu, Z., et al. (2015). 3D ShapeNets: A deep representation for volumetric shapes. In CVPR.
[51] Zemel, R., et al. (2013). Learning fair representations. In ICML.
