Author: Liang Lin
Publisher: Springer Nature
ISBN: 9811323879
Category : Computers
Languages : en
Pages : 156
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Book Description
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.
Author: Thomas B. Moeslund
Publisher: Springer Science & Business Media
ISBN: 0857299972
Category : Computers
Languages : en
Pages : 633
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Book Description
This unique text/reference provides a coherent and comprehensive overview of all aspects of video analysis of humans. Broad in coverage and accessible in style, the text presents original perspectives collected from preeminent researchers gathered from across the world. In addition to presenting state-of-the-art research, the book reviews the historical origins of the different existing methods, and predicts future trends and challenges. Features: with a Foreword by Professor Larry Davis; contains contributions from an international selection of leading authorities in the field; includes an extensive glossary; discusses the problems associated with detecting and tracking people through camera networks; examines topics related to determining the time-varying 3D pose of a person from video; investigates the representation and recognition of human and vehicular actions; reviews the most important applications of activity recognition, from biometrics and surveillance, to sports and driver assistance.
Author: Fabio Solari
Publisher: BoD – Books on Demand
ISBN: 9535105639
Category : Computers
Languages : en
Pages : 192
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Book Description
Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans.
Author: Kevin Lin
Publisher:
ISBN:
Category :
Languages : en
Pages : 131
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Book Description
Supervised training with deep Convolutional Neural Networks (CNNs) have achieved great success in various visual recognition tasks. However, supervised training with deep CNNs requires large amount of well-annotated data. Data labeling, especially for large-scale image dataset, is very expensive. How to learn an effective model without the need of training data labeling has become an important problem for many applications. A promising solution is to create a learning protocol for the neural networks, so that the neural networks can learn to teach itself without manual labels. This technique is referred as the self-supervised learning, which has recently drawn an increasing attention for improving the learning performance. In this thesis, we first present our work on learning binary descriptors for fast image retrieval without manual labeling. We observe that images with the same category should have similar visual textures, and these similar textures are usually invariant to shift, scale and rotation. Thus, we could generate similar texture patch pairs automatically for training CNNs by shifting, scaling, and rotating image patches. Based on the observation, we design a training protocol for deep CNNs, which automatically generates pair-wise pseudo labels describing the similarity between the given two images. The proposed method performs more favorably than the baselines on different tasks including patch matching, image retrieval, and object recognition. In the second part of this thesis, we turn our focus to the task of human-centric analysis applications, and present our work on learning multi-person part segmentation without human labeling. Our proposed complementary learning technique learns a neural network model for multi-person part segmentation using a synthetic dataset and a real dataset. We observe that real and synthetic humans share a common skeleton structure. During learning, the proposed model extracts human skeletons which effectively bridges the synthetic and real domains. Without using human-annotated part segmentation labels, the resultant model works well on real world images. Our method outperforms the state-of-the-art approaches on multiple public datasets. Then, we discuss our work on accelerating multi-person pose estimation using a proposed concatenated pyramid network. We observe that each image may contain an unknown number of people that can occur at any scale or position. This makes fast multi-person pose estimation very challenging. Different from the earlier deep learning approaches that extract image features by using a series of convolutions, our proposed method extracts image features from each convolution layer in parallel, which better captures image features in different scales and improve the performance of human pose estimation. Our proposed method eliminates the need of multi-scale inference and multi-stage detection, and the proposed method is many times faster than the state-of-the-art approaches, while achieving better accuracy on the public datasets. Next, we present our work on 3D human mesh construction from a single image. We propose a novel approach to learn the human mesh representation without any ground truth mesh. This is made possible by introducing two new terms into the loss function of a graph convolutional neural network (Graph CNN). The first term is the Laplacian prior that acts as a regularizer on the mesh construction. The second term is the part segmentation loss that forces the projected region of the constructed mesh to match the part segmentation. Experimental results on multiple public datasets show that without using 3D ground truth meshes, the proposed approach outperforms the previous state-of-the-art approaches that require 3D ground truth meshes for training. Finally, we summarize our completed works and discuss the future research directions.
Author: Mohamed Hamlich
Publisher: Springer Nature
ISBN: 3030451836
Category : Computers
Languages : en
Pages : 366
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Book Description
This volume constitutes refereed proceedings of the Third International Conference on Smart Applications and Data Analysis, SADASC 2020, held in Marrakesh, Morocco. Due to the COVID-19 pandemic the conference has been postponed to June 2020. The 24 full papers and 3 short papers presented were thoroughly reviewed and selected from 44 submissions. The papers are organized according to the following topics: ontologies and meta modeling; cyber physical systems and block-chains; recommender systems; machine learning based applications; combinatorial optimization; simulations and deep learning.
Author: Dan Zhang
Publisher: Elsevier
ISBN: 0128223154
Category : Technology & Engineering
Languages : en
Pages : 282
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Book Description
Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length. Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems Demonstrates computational techniques for control systems Covers iterative learning impedance control in both human-robot interaction and collaborative robots
Author: Andrew Glassner
Publisher: No Starch Press
ISBN: 1718500734
Category : Computers
Languages : en
Pages : 1315
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Book Description
A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: • How text generators create novel stories and articles • How deep learning systems learn to play and win at human games • How image classification systems identify objects or people in a photo • How to think about probabilities in a way that's useful to everyday life • How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it. Full Color Illustrations
Author: S. M. Mahbubur Rahman
Publisher: Springer Nature
ISBN: 9813299452
Category : Computers
Languages : en
Pages : 149
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Book Description
Instead of focusing on the mathematical properties of moments, this book is a compendium of research that demonstrates the effectiveness of orthogonal moment-based features in face recognition, expression recognition, fingerprint recognition and iris recognition. The usefulness of moments and their invariants in pattern recognition is well known. What is less well known is how orthogonal moments may be applied to specific problems in human-centric visual pattern recognition. Unlike previous books, this work highlights the fundamental issues involved in moment-based pattern recognition, from the selection of discriminative features in a high-dimensional setting, to addressing the question of how to classify a large number of patterns based on small training samples. In addition to offering new concepts that illustrate the use of statistical methods in addressing some of these issues, the book presents recent results and provides guidance on implementing the methods. Accordingly, it will be of interest to researchers and graduate students working in the broad areas of computer vision and visual pattern recognition.
Author: Ben Shneiderman
Publisher: Oxford University Press
ISBN: 0192845292
Category : Computers
Languages : en
Pages : 390
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Book Description
The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology. In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education, accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.
Author: Stephen Muggleton
Publisher: Oxford University Press
ISBN: 0192607464
Category : Computers
Languages : en
Pages : 560
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Book Description
In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s-2030s), and is being actively investigated in the US, Europe and China. The chapters of this book have been authored by a mixture of UK and other international specialists. Some of the key questions addressed by the Human-Like Computing programme include how AI systems might 1) explain their decisions effectively, 2) interact with human beings in natural language, 3) learn from small numbers of examples and 4) learn with minimal supervision. Solving such fundamental problems involves new foundational research in both the Psychology of perception and interaction as well as the development of novel algorithmic approaches in Artificial Intelligence.