Author:
Dhabliya Dharmesh,Ugli Ibrokhimov Sarvar Muydinjon,Murali M. J.,Abbas Ahmed H. R.,Gulbahor Uralova
Abstract
The topic of computer vision has emerged as one that is fast developing, altering how we examine and comprehend pictures and movies. Image and video analysis has significantly advanced in recent years, opening the door for applications in a variety of industries including healthcare, robotics, surveillance, and autonomous systems. An overview of current developments in computer vision methods, algorithms, and techniques used in image and video analysis is given in this abstract. In conclusion, there have been tremendous improvements in image and video analysis in the field of computer vision. Recurrent neural networks (RNNs) and CNNs are two examples of deep learning approaches that have been used to enhance accuracy, resilience, and efficiency in a variety of applications. A richer comprehension of visual material has resulted from the integration of spatial and temporal data with semantic analysis. These developments have enormous potential for use in a variety of fields, influencing the direction of computer vision and its social effects.
Reference35 articles.
1. Ren S., He K., Girshick R., & Sun J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (pp. 91–99).
2. Liu W., Anguelov D., Erhan D., Szegedy C., Reed S., Fu C.Y., & Berg A.C. (2016). SSD: Single shot multibox detector. In European Conference on Computer Vision (pp. 21–37).
3. Redmon J., Divvala S., Girshick R., & Farhadi A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779–788).
4. Long J., Shelhamer E., & Darrell T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3431–3440).
5. Ronneberger O., Fischer P., & Brox T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention (pp. 234–241).
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Advancements and Integration in Computer Vision and Graphics Systems;International Journal of Advanced Research in Science, Communication and Technology;2024-07-21
2. Ovarian Cancer Detection Using Computer Vision;2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH);2024-03-20
3. Rethinking False Positives with Class Activation Maps: Can we define false positives without ground-truths?;2023 International Conference on Electrical, Communication and Computer Engineering (ICECCE);2023-12-30