This book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions. Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding. This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding.
Inspired by the community behaviors of animals and humans, cooperative control has been intensively studied by numerous researchers in recent years. Cooperative control aims to build a network system collectively driven by a global objective function in a distributed or centralized communication network and shows great application potential in a wide domain. From the perspective of cybernetics in network system cooperation, one of the main tasks is to design the formation control scheme for multiple intelligent unmanned systems, facilitating the achievements of hazardous missions – e.g., deep space exploration, cooperative military operation, and collaborative transportation. Various challenges in such real-world applications are driving the proposal of advanced formation control design, which is to be addressed to bring academic achievements into real industrial scenarios. This book extends the performance of formation control beyond classical dynamic or stationary geometric configurations, focusing on formation maneuverability that enables cooperative systems to keep suitable spacial configurations during agile maneuvers. This book embarks on an adventurous journey of maneuverable formation control in constrained space with limited resources, to accomplish the exploration of an unknown environment. The investigation of the real-world challenges, including model uncertainties, measurement inaccuracy, input saturation, output constraints, and spatial collision avoidance, brings the value of this book into the practical industry, rather than being limited to academics.
Previous research on fixed/finite-time sliding-mode control focuses on forcing a system state (vector) to converge within a certain time moment, regardless of how each state element converges. This book introduces a control problem with unique finite/fixed-time stability considerations, namely time-synchronized stability, where at the same time, all the system state elements converge to the origin, and fixed-time-synchronized stability, where the upper bound of the synchronized settling time is invariant with any initial state. Accordingly, sufficient conditions for (fixed-) time-synchronized stability are presented. These stability formulations grant essentially advantageous performance when a control system (with diversified subsystems) is expected to accomplish multiple actions synchronously, e.g., grasping with a robotic hand, multi-agent simultaneous cooperation, etc. Further, the analytical solution of a (fixed) time-synchronized stable system is obtained and discussed. Applications to linear systems, disturbed nonlinear systems, and network systems are provided. In addition, comparisons with traditional fixed/finite-time sliding mode control are suitably detailed to showcase the full power of (fixed-) time-synchronized control.
Originated from the Andeans and characterized by its high nutritional value and wide adaptability, the potato is one of the most globally important crops today. It is now being planted in about 160 countries, with China as the biggest potato producer in the world, accounting for one-fourth of the world's total production. The production and consumption of potatoes in China will influence the potato industry worldwide. This book covers the origin, distribution, production, and consumption of the potato. Potatoes have been planted throughout China since their introduction 400 years ago. They have become the main staple food for many Chinese people, especially in the remote mountainous regions. Through the years, different regions have developed different cooking methods for this highly versatile vegetable. With increasing concerns over the ills of Western processed food, for example, French fries, potato dishes prepared in the Chinese way have the advantage of being healthy and diverse. This book serves to provide detailed instructions for their many methods of preparation.With 2008 declared as the United Nations International Year of the Potato, the release of this book is timely, as it encourages healthy and more varied consumption of the crop, thus advancing the potato industry forward.
This book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions. Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding. This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding.
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