Deep Learning Techniques for Image Segmentation and Data Annotation

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Abstract
This chapter explores the integration of deep learning techniques in image segmentation, emphasizing state-of-the-art architectures, such as FCN, U-Net, and Vision Transformers, that enable precise and efficient segmentation for a range of applications. It discusses the significance of data annotation in training deep learning models, highlighting methods like active learning, semi-supervised learning, and automated annotation. The chapter also addresses key challenges, including limited labeled data, model size constraints, and performance on edge devices. Additionally, it covers the diverse use cases of image segmentation across fields like medical imaging, agriculture, and civil. Finally, the chapter outlines emerging trends in the field, including self-supervised learning, federated learning, and the development of lightweight models for real-time applications, offering insights into the future potential and impact of deep learning in image segmentation.

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