Bøger af Antonio Torralba

An accessible, authoritative, and up-to-date computer vision textbook offering a comprehensive introduction to the foundations of the field that incorporates the latest deep learning advances.Machine learning has revolutionized computer vision, but the methods of today have deep roots in the history of the field. Providing a much-needed modern treatment, this accessible and up-to-date textbook comprehensively introduces the foundations of computer vision while incorporating the latest deep learning advances. Taking a holistic approach that goes beyond machine learning, it addresses fundamental issues in the task of vision and the relationship of machine vision to human perception. Foundations of Computer Vision covers topics not standard in other texts, including transformers, diffusion models, statistical image models, issues of fairness and ethics, and the research process. To emphasize intuitive learning, concepts are presented in short, lucid chapters alongside extensive illustrations, questions, and examples. Written by leaders in the field and honed by a decade of classroom experience, this engaging and highly teachable book offers an essential next-generation view of computer vision.  Up-to-date treatment integrates classic computer vision and deep learning Accessible approach emphasizes fundamentals and assumes little background knowledgeStudent-friendly presentation features extensive examples and imagesProven in the classroomInstructor resources include slides, solutions, and source code

This book presents a thorough state-of-the-art review for internally compensated Low Dropout Regulators (IC-LDO). the Flipped Voltage Follower (FVF), which has been recently applied in the design of internally compensated LDOs to enhance their performances.

This book presents a thorough state-of-the-art review for internally compensated Low Dropout Regulators (IC-LDO). the Flipped Voltage Follower (FVF), which has been recently applied in the design of internally compensated LDOs to enhance their performances.