Computer Vision in Science: Deep Learning Algorithms and Practical Research Projects - Julio Cesar Rodriguez-Quiñonez

This book provides a rigorous and current treatment of modern computer vision by linking algorithms with recent advances in artificial intelligence (AI). The book addresses key themes such as representation learning and foundation models, generative and data-centric approaches, and multimodal integration, as well as advanced image processing and embedded vision, while also examining deployment considerations, trustworthiness (explainability and robustness), and evaluation practices. Through real world examples in areas such as medical imaging, environmental remote sensing, robotics, and autonomous systems, and adopting a scientific approach, it balances theoretical insights with practical case studies. Designed for researchers, graduate students, and professionals, this book offers a structured perspective on current algorithms and methods for computer vision.