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.
TopIntroduction
Image processing involves various techniques and algorithms applied to digital images to achieve desired outcomes. These techniques range from basic operations like enhancement and noise reduction to complex tasks like segmentation, object recognition, and classification. Image processing is crucial in fields like medical imaging, surveillance, remote sensing, robotics, and computer vision. Recent research has focused on image processing and AI/ML, with advancements in various aspects. Key research areas within the intersection of image processing and AI/ML include image segmentation, object recognition, and image classification(Barrett, 2023; Erickson, 2021).
Deep learning has revolutionized image analysis by enabling convolutional neural networks (CNNs) to learn hierarchical representations of images. This enables tasks like image classification, object detection, and semantic segmentation. Research focuses on efficient models, network architectures, and techniques for handling limited training data and interpretability of learned features(Santosh et al., 2022). Generative models like GANs and VAEs are popular in image synthesis, generating realistic images from noise or manipulating existing images. Research focuses on improving image quality, diversity, mode collapse issues, and enabling better control over the synthesis process(Harikaran et al., 2023; Janardhana, Singh, et al., 2023; Reddy et al., 2023). Transfer learning and domain adaptation techniques transfer pre-trained models from one domain to another, enabling more accurate and efficient models for various applications in image processing. This research area focuses on developing methods to transfer learned features from large-scale datasets to specific tasks with limited labeled data, enhancing the overall efficiency of image analysis(Balyen & Peto, 2019).
AI/ML models' decision-making process becomes crucial as they become more complex. Research focuses on developing techniques to explain and interpret these decisions in image processing tasks. This includes visualizing learned features, identifying important regions for predictions, and providing insights into model behavior, enhancing trust and transparency(Anitha et al., 2023; Boopathi, 2023b; Selvakumar et al., 2023; Subha et al., 2023). Traditional image processing methods often require large amounts of labeled data for training, which can be expensive and time-consuming. Research in weakly supervised and unsupervised learning aims to develop methods that learn from weak or unlabeled data, including self-supervised learning, semi-supervised learning, and active learning. These techniques explore ways to leverage unlabeled or partially labeled data for training image processing models(Boopathi, 2023a; Boopathi, Arigela, et al., 2023; Boopathi, Venkatesan, et al., 2023; Kavitha et al., 2023; Yupapin et al., 2023). Research in image processing is crucial to ensure robustness of AI/ML models against adversarial attacks. These attacks introduce imperceptible perturbations, leading to misclassification or incorrect behavior. The focus is on developing defense mechanisms, improving model robustness, and investigating vulnerabilities and limitations in AI/ML models. Researchers are working on AI/ML techniques to enhance image processing accuracy, efficiency, interpretability, and robustness, benefiting various fields like healthcare, robotics, and autonomous vehicles.