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Image segmentation as a book category covers the theory, methods, tools, and applications for dividing images into meaningful regions or objects — from classical techniques (thresholding, clustering, graph cuts, region growing) to modern deep-learning approaches (FCNs, U-Nets, Mask R-CNN, transformers) and their evaluation. Books in this category include textbooks and primers explaining fundamentals and math, practical programming guides with code and datasets, research monographs on advanced models and architectures, and application-focused volumes for domains like medical imaging, remote sensing, robotics, and autonomous driving. They also address practical challenges such as annotation, benchmark metrics, model interpretability, domain adaptation, and computational efficiency, making the category useful for students, researchers, and practitioners working on image analysis and computer vision.

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