MACHINE LEARNING FOR IMAGE RECOGNITION AND COMPUTER VISION: STATE-OF-THE-ART TECHNIQUES AND APPLICATIONS

Abstract

Modern techniques and a wide range of applications across numerous fields are the consequence of machine learning's transformation of computer vision and image recognition. The implications of "machine learning for image recognition, and computer vision in the aspect of state of the art, and applications" are the main topic of discussion in this research.

Literature review: The literature study examines cutting-edge techniques and applications; machine learning is crucial for image identification. Convolutional Neural Networks (CNNs) have revolutionised the field and become extremely proficient at tasks like object detection and facial identification by enabling automatic feature extraction and hierarchical pattern recognition.

Methodology: A range of internet resources have been employed in the research to collect data, which is then subjected to "theoretical analysis." The theoretical analysis phase is crucial since it broadens the understanding of the subject.

Findings: The study has employed "thematic analysis" in addition to data collecting to further analyse the data collected. Furthermore, theoretical analysis serves as a helpful tool in this research because it promotes the development of the area and makes advanced information easier to obtain.

Discussion: The paper provides a comprehensive analysis of the impact of machine learning on image identification and computer vision.

Conclusion: The study investigates how computer vision and image recognition are significantly impacted by machine learning.