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

Aug 2, 2026

Modern machine learning methods are examined for their transformation of computer vision and image recognition, with a focus on state-of-the-art approaches and real-world applications. Convolutional Neural Networks (CNNs) are highlighted as the basis for learning spatial hierarchies and extracting hierarchical features. Transfer learning is reviewed alongside object detection, semantic segmentation, and real-time deployment. The study also analyzes impacts across security, healthcare diagnostics, agriculture monitoring, and autonomous driving, while considering ethical challenges such as privacy, bias, and responsible AI.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

Aim

Research Objectives

Research Questions

Literature Review

Methodology

Findings

Discussion

Conclusion

Journal of Advanced Zoology ISSN: 0253-7214 Volume 45 Special Issue 02 Year 2023 Page 1645:1655 MACHINE LEARNING FOR IMAGE RECOGNITION AND COMPUTER VISION: STATE-OF-THE-ART TECHNIQUES AND APPLICATIONS Dr. Dattatreya P Mankame Department: Computer science and Business Systems Institute: Dayananda Sagar college of Engineering District: Bangalore City: Bangalore State: Karnataka [Emailid: ](Emailid: dpmankame@gmail.com)[dpmankame@gmail.com](Emailid: dpmankame@gmail.com) Dr Shubhangi Dnyaneshwar Kirange Government Polytechnic Jalgaon Jalgaon, Maharashtra G.HUBERT Assistant professor Department of computer science I.V.E.T. College Gowrivakkam KUWAR PRATAP SINGH Designation: Assistant Professor Department: Department of Computer Science and Engineering Institute: Faculty of Engineering and Technology, SRM Institute of Science and Technology, Delhi-NCR Campus, Modinagar District: Ghaziabad, City: Ghaziabad, State: Uttar Pradesh Dr. Prakash Tanaji Wankhedkar Assistant Professor, Department of Zoology, M.J.M. Arts, Commerce and Science College Karanjali Tal Peth Dist Nashik 422008 [wankhedkarpt@gmail.com](wankhedkarpt@gmail.com) | Article History
Received: 12 March 2023
Revised: 21 August 2023 Accepted: 09 October 2023 | Abstract
Introduction: 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 | | --- | --- | 1645 Available online at: [https://jazindia.com](https://jazindia.com) | CC License
CC-BY-NC-SA 4.0 | 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.
Keywords: Machine learning, computer vision, convolutional neural networks, image recognition applications | | --- | --- | Introduction Machine learning has transformed computer vision and image identification, resulting in modern methods and a wide range of applications in several fields. The research focuses on discussing the implications of “machine learning for image recognition, and computer vision in the aspect of state of the art, and applications”. The foundation of picture recognition has been established by Convolutional Neural Networks (CNNs), which allow for the learning of spatial hierarchies and the extraction of hierarchical features. Therefore, the role of CNN in the context of machine learning impacts on image recognition will be critically evaluated in this research. Transfer learning, which saves time and computational resources by fine-tuning pretrained models for particular tasks, has become widely used. As suggested by Hou et al. (2021), accurate and effective object localisat

FAQs

What role do CNNs play in image recognition within this study?

CNNs are presented as the foundation for picture recognition, enabling learning of spatial hierarchies and extraction of hierarchical features. Their impact on machine learning-driven image recognition is critically evaluated.

How does transfer learning contribute to practical computer vision tasks?

Transfer learning speeds up development and reduces computational cost by fine-tuning pretrained models for specific tasks. It supports improved object localisation in detection models such as Faster R-CNN, YOLO, and SSD.

Which applications and ethical issues are emphasized for computer vision?

Applications include facial identification for security, illness detection for healthcare, crop monitoring for agriculture, and navigation support for autonomous cars. Ethical challenges include privacy preservation, bias mitigation, and responsible AI deployment.

Aug 2, 2026

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