Advancements in Image Processing Techniques for Computer Vision Applications

Year : 2024 | Volume :11 | Issue : 01 | Page : 27-32
By

    Jayshree Dwivedi

  1. Pragya Rajput

  1. Assistant Professor, Department of Computer Science Engineering, Corporate Institute of Science & Technology, Bhopal, Madhya Pradesh, India
  2. Assistant Professor, Department of Computer Science Engineering, Corporate Institute of Science & Technology, Bhopal, Madhya Pradesh, India

Abstract

The fast-developing field of computer vision is transforming how people perceive and comprehend pictures and movies. Autonomous systems, robotics, healthcare, and surveillance are just a few of the many applications that have been made possible by recent significant advances in image and video processing. An overview of current developments in computer vision approaches, algorithms, and techniques for image and video analysis is given in this abstract. In conclusion, the analysis of images and videos has advanced significantly in the field of computer vision. Recurrent neural networks (RNNs) and CNNs are two examples of deep learning approaches that have been used to increase accuracy, robustness, and efficiency in a variety of applications. A richer understanding has resulted from the integration of semantic analysis with temporal and spatial data.

Keywords: Image processing, CNN, GANs, machine learning, RNN

[This article belongs to Journal of Image Processing & Pattern Recognition Progress(joipprp)]

How to cite this article: Jayshree Dwivedi, Pragya Rajput.Advancements in Image Processing Techniques for Computer Vision Applications.Journal of Image Processing & Pattern Recognition Progress.2024; 11(01):27-32.
How to cite this URL: Jayshree Dwivedi, Pragya Rajput , Advancements in Image Processing Techniques for Computer Vision Applications joipprp 2024 {cited 2024 Apr 03};11:27-32. Available from: https://journals.stmjournals.com/joipprp/article=2024/view=138415


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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received January 25, 2024
Accepted February 29, 2024
Published April 3, 2024
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