Image Intelligence Principles and Applications

Image Intelligence: Principles and Applications offers a practical foundation for understanding how images can be transformed into meaningful insights using modern AI. The book explains essential principles such as image formation, sampling, color spaces, filtering, feature extraction, and representation learning, then connects them to today’s deep learning methods including CNNs, transfer learning, and attention-based vision models. Readers gain step-by-step knowledge of building complete image intelligence pipelines data collection, preprocessing, augmentation, model training, evaluation, and deployment. Application-focused chapters cover medical diagnosis support, remote sensing and land monitoring, surveillance and activity understanding, industrial quality inspection, document analysis, and smart retail. The text also highlights explainability, bias, robustness, and responsible use of image analytics in real environments. With clear illustrations, case studies, and hands-on exercises, this book is ideal for students, researchers, and practitioners who want to master the principles and real-world applications of intelligent image analysis.