Angielski
Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasising machine and deep learning techniques that have come to the fore during the past 5–10 years. It provides clear explanations of principles and their algorithms supported by applications to demonstrate how vital methods and techniques have been put into practice. Classical topics are presented and taken further by emerging techniques; for example, the impact of learning machines and DNNs in 3D vision and motion. This book provides easy learning for the researcher and practitioner of advanced computer vision methods, and is a suitable textbook for a second course on computer vision, or a deep learning course, for the advanced undergraduate and graduate student. Topics include: achine learning; deep learning networks; generative adversarial networks; deep reinforcement learning; self-supervised learning; extraction of robust features; object detection; semantic segmentation; linguistic descriptions of images; visual search; visual tracking; 3D shape retrieval; image inpainting; novelty and anomaly detection. A reference on deep learning and advanced computer methods by leading people in the fieldIllustrates the principles with modern, real world applicationsSuitable self-learning and as a text for graduate courses
