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Computer vision promising innovations
- 1 Capitol Technology University
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Abstract
Computer vision, an interdisciplinary field bridging artificial intelligence and image processing, seeks to bestow machines with the capability to interpret and make decisions based on visual data. As the digital age propels forward, the ubiquity of visual content underscores the importance of efficient and effective automated interpretation. This paper delves deeply into the modern advancements and methodologies of computer vision, emphasizing its transformative role in various applications ranging from medical imaging to autonomous driving. With the increasing complexity of visual data, challenges arise pertaining to real-time processing, scalability, and the ethical implications of automated decision-making. Through an exhaustive literature review and novel experimentation, this research demystifies the multifaceted domain of computer vision, elucidating its potential and constraints. The study culminates in a visionary outlook, highlighting future avenues for research, including the fusion of augmented reality with computer vision, novel deep learning architectures, and ensuring ethical AI practices in visual interpretation.
Keywords
computer vision, artificial intelligence, real-time processing, deep learning, ethical AI
[1]. Zhang, X., Sclaroff, S., & Lin, Z. (2018). Learning Interpretable Features via Adversarial Training. IEEE Transactions on Image Processing, 27(8), 3767-3778.
[2]. Jiang, F., Jiang, Y., & Zhang, K. (2020). Artificial intelligence in healthcare: past, present, and future. Stroke and Vascular Neurology, 4(4), 230-243.
[3]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1106-1114.
[4]. Goodfellow, I., Pouget-Abadie, J., & Bengio, Y. (2014). Generative adversarial networks. arXiv preprint arXiv:1406.2661.
[5]. Dosovitskiy, A., Beyer, L., & Koltun, V. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
[6]. Shen, D., Wu, G., & Suk, H. I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221-248.
[7]. Chen, Y., Liu, Y., & Zhang, X. (2017). Deep learning-based
[8]. Certainly! Here are ten additional references, as well as two illustrative tables for the computer vision topic.
[9]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
[10]. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016). Region-based convolutional networks for accurate object detection and segmentation. IEEE transactions on pattern analysis and machine intelligence, 38(1), 142-158.
[11]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.
[12]. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788.
[13]. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention
Cite this article
Ali,Y.M.D. (2023). Computer vision promising innovations. Advances in Engineering Innovation,3,5-8.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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