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Applications of Artificial Intelligent and Machine Learning Techniques in Image Processing

Applications of Artificial Intelligent and Machine Learning Techniques in Image Processing

Sampath Boopathi, Uday Kumar Kanike
Copyright: © 2023 |Pages: 23
ISBN13: 9781668486184|ISBN10: 1668486180|ISBN13 Softcover: 9781668486191|EISBN13: 9781668486207
DOI: 10.4018/978-1-6684-8618-4.ch010
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MLA

Boopathi, Sampath, and Uday Kumar Kanike. "Applications of Artificial Intelligent and Machine Learning Techniques in Image Processing." Handbook of Research on Thrust Technologies’ Effect on Image Processing, edited by Binay Kumar Pandey, et al., IGI Global, 2023, pp. 151-173. https://doi.org/10.4018/978-1-6684-8618-4.ch010

APA

Boopathi, S. & Kanike, U. K. (2023). Applications of Artificial Intelligent and Machine Learning Techniques in Image Processing. In B. Pandey, D. Pandey, R. Anand, D. Mane, & V. Nassa (Eds.), Handbook of Research on Thrust Technologies’ Effect on Image Processing (pp. 151-173). IGI Global. https://doi.org/10.4018/978-1-6684-8618-4.ch010

Chicago

Boopathi, Sampath, and Uday Kumar Kanike. "Applications of Artificial Intelligent and Machine Learning Techniques in Image Processing." In Handbook of Research on Thrust Technologies’ Effect on Image Processing, edited by Binay Kumar Pandey, et al., 151-173. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-8618-4.ch010

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Abstract

This chapter explores the role of AI and machine learning (ML) in image processing, focusing on their applications. It covers AI techniques like supervised learning, unsupervised learning, reinforcement learning, and deep learning. AI techniques include rule-based systems, expert systems, fuzzy logic, and genetic algorithms. Machine learning techniques include SVM, decision trees, random forests, K-means clustering, and PCA. Deep learning techniques like CNN, RNN, and GANs are used in tasks like object recognition, classification, and segmentation. The chapter emphasizes the impact of AI and ML on accuracy, efficiency, and decision-making. It also discusses evaluation metrics and performance analysis, emphasizing the importance of selecting appropriate metrics and techniques. The chapter also addresses ethical considerations, such as fairness, privacy, transparency, and human-AI collaboration.

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