Jonathan Wu – författare

The problem of structure and motion recovery from image sequences is an important theme in computer vision. Considerable progress has been made in this field during the past two decades, resulting in successful applications in robot navigation, augmented reality, industrial inspection, medical image analysis, and digital entertainment, among other areas. However, many of these methods work only for rigid objects and static scenes. The study of non-rigid structure from motion is not only of academic significance, but also has important practical applications in real-world, nonrigid or dynamic scenarios, such as human facial expressions and moving vehicles.This practical guide/reference provides a comprehensive overview of Euclidean structure and motion recovery, with a specific focus on factorization-based algorithms. The book discusses the latest research in this field, including the extension of the factorization algorithm to recover the structure of non-rigid objects, and presents some new algorithms developed by the authors. Readers require no significant knowledge of computer vision, although some background on projective geometry and matrix computation would be beneficial.Topics and features: presents the first systematic study of structure and motion recovery of both rigid and non-rigid objects from images sequences; discusses in depth the theory, techniques, and applications of rigid and non-rigid factorization methods in three dimensional computer vision; examines numerous factorization algorithms, covering affine, perspective and quasi-perspective projection models; provides appendices describing the mathematical principles behind projective geometry, matrix decomposition, least squares, and nonlinear estimation techniques; includes chapter-ending review questions, and a glossary of terms used in the book.This unique text offers practical guidance in real applications and implementations of 3D modeling systems for practitioners incomputer vision and pattern recognition, as well as serving as an invaluable source of new algorithms and methodologies for structure and motion recovery for graduate students and researchers.

This comprehensive analysis of COVID-19 draws on the Iceberg Framework—a modified systems thinking model—to examine the deeper forces that shaped the pandemic's trajectory, from structural inequities and institutional fragility to shifting mental models and global behavioral patterns.Authored by a former Taiwanese Minister of Health and an experienced health system leader, the book combines personal insight with international perspective, offering historical context, contemporary data, and strategic foresight. It situates COVID-19 among history's deadliest pandemics while critically exploring the events, systemic structures, and cultural responses that influenced the crisis. Structured in four parts, the book journeys from echoes of past pandemics and variant-driven disruptions to the hidden foundations of public health responses, culminating in a forward-looking reflection on how COVID-19 has reshaped our understanding of pandemic risk and resilience, while also offering readers a framework for enhanced preparedness in an era of converging global threats.Timely, accessible, and analytically rigorous, Decoding the Pandemic is essential reading for students, researchers, and practitioners in public health, epidemiology, global health policy, and systems science—anyone seeking to understand not just what happened, but why, and what must change before the next pandemic arrives.

This comprehensive analysis of COVID-19 draws on the Iceberg Framework—a modified systems thinking model—to examine the deeper forces that shaped the pandemic's trajectory, from structural inequities and institutional fragility to shifting mental models and global behavioral patterns.Authored by a former Taiwanese Minister of Health and an experienced health system leader, the book combines personal insight with international perspective, offering historical context, contemporary data, and strategic foresight. It situates COVID-19 among history's deadliest pandemics while critically exploring the events, systemic structures, and cultural responses that influenced the crisis. Structured in four parts, the book journeys from echoes of past pandemics and variant-driven disruptions to the hidden foundations of public health responses, culminating in a forward-looking reflection on how COVID-19 has reshaped our understanding of pandemic risk and resilience, while also offering readers a framework for enhanced preparedness in an era of converging global threats.Timely, accessible, and analytically rigorous, Decoding the Pandemic is essential reading for students, researchers, and practitioners in public health, epidemiology, global health policy, and systems science—anyone seeking to understand not just what happened, but why, and what must change before the next pandemic arrives.

The problem of structure and motion recovery from image sequences is an important theme in computer vision. Considerable progress has been made in this field during the past two decades, resulting in successful applications in robot navigation, augmented reality, industrial inspection, medical image analysis, and digital entertainment, among other areas. However, many of these methods work only for rigid objects and static scenes. The study of non-rigid structure from motion is not only of academic significance, but also has important practical applications in real-world, nonrigid or dynamic scenarios, such as human facial expressions and moving vehicles.This practical guide/reference provides a comprehensive overview of Euclidean structure and motion recovery, with a specific focus on factorization-based algorithms. The book discusses the latest research in this field, including the extension of the factorization algorithm to recover the structure of non-rigid objects, and presents some new algorithms developed by the authors. Readers require no significant knowledge of computer vision, although some background on projective geometry and matrix computation would be beneficial.Topics and features: presents the first systematic study of structure and motion recovery of both rigid and non-rigid objects from images sequences; discusses in depth the theory, techniques, and applications of rigid and non-rigid factorization methods in three dimensional computer vision; examines numerous factorization algorithms, covering affine, perspective and quasi-perspective projection models; provides appendices describing the mathematical principles behind projective geometry, matrix decomposition, least squares, and nonlinear estimation techniques; includes chapter-ending review questions, and a glossary of terms used in the book.This unique text offers practical guidance in real applications and implementations of 3D modeling systems for practitioners incomputer vision and pattern recognition, as well as serving as an invaluable source of new algorithms and methodologies for structure and motion recovery for graduate students and researchers.

This book contains some selected papersfrom the International Conference on Extreme Learning Machine 2015,which was held in Hangzhou, China,December 15-17,2015.This conference brought together researchers and engineers to share andexchange R&D experience on both theoretical studies and practicalapplications of the Extreme Learning Machine (ELM) technique and brainlearning.This book covers theories, algorithms adapplications of ELM. It gives readers a glance of the most recent advances ofELM.

This book contains some selected papersfrom the International Conference on Extreme Learning Machine 2015,which was held in Hangzhou, China,December 15-17,2015.This conference brought together researchers and engineers to share andexchange R&D experience on both theoretical studies and practicalapplications of the Extreme Learning Machine (ELM) technique and brainlearning. This book covers theories, algorithms adapplications of ELM. It gives readers a glance of the most recent advances ofELM.

This book contains some selected papersfrom the International Conference on Extreme Learning Machine 2015,which was held in Hangzhou, China,December 15-17,2015.