| Issue |
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
|
|
|---|---|---|
| Article Number | 04045 | |
| Number of page(s) | 8 | |
| Section | Computer Science | |
| DOI | https://doi.org/10.1051/e3sconf/202339904045 | |
| Published online | 12 July 2023 | |
Computer Vision: Advances in Image and Video Analysis
1 Professor, Department of Information Technology, Vishwakarma Institute of Information Technology, Pune, Maharashtra, India
2 National University Of Uzbekistan
3 Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai – 127
4 College of technical engineering, The Islamic university, Najaf, Iraq
5 Tashkent State Pedagogical University, Tashkent, Uzbekistan
* Correspondingauthor: dharmesh.dhabliya@viit.ac.in
ibroximovsarvar0@gmail.com
m.j.murali_eee@psvpec.in
ahmedabbas85@iunajaf.edu.iq
gulbahoruralova1@gmail.com
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.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.