Orthogonal Image Moments for Human-Centric Visual Pattern Recognition, S. M. Mahbubur Rahman; Tamanna Howlader; Dimitrios
Автор: James J. (Jong Hyuk) Park; Qun Jin; Martin Sang-so Название: Human Centric Technology and Service in Smart Space ISBN: 9400795599 ISBN-13(EAN): 9789400795594 Издательство: Springer Рейтинг: Цена: 32004.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The theme of HumanCom is focused on the various aspects of human-centric computing for advances in computer science and its applications and provides an opportunity for academic and industry professionals to discuss the latest issues and progress in the area of human-centric computing.
Описание: Comprising papers on diverse aspects of bio-inspired models, soft computing and hybrid intelligent systems, the articles in this book are divided into four main parts that cover important research areas such as fuzzy and bio-inspired problem-solving models.
Автор: Liang Lin Название: Human Centric Visual Analysis with Deep Learning ISBN: 9811323860 ISBN-13(EAN): 9789811323867 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Описание: A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the ""average."" From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.
Описание: Whether an old photograph or a single video frame, there is a wealth of data hidden in a picture. Image processing and pattern analysis play a vital role in engineering science and can be applied in diverse areas to solve existing and practical problems.The Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing discusses the advances of image processing and pattern analysis and addresses how new innovations will cater to the demands of daily life. This handbook provides the resources necessary for technology developers, scientists, and policymakers to adopt and implement new inventions across the globe.The chapters presented in this publication encompass various aspects of recent image processing and pattern analysis innovations including, but not limited to, mobile image tracking, motion picture analysis, pattern classification, multisensory data fusion, 3D imaging, supporting routing protocols, brain computer interfaces, image restoration, and medical imaging.
Описание: This three-book set constitutes the refereed proceedings of the Second International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018, held in Solapur, India, in December 2018.
The 173 revised full papers presented were carefully reviewed and selected from 374 submissions. The papers are organized in topical sections in the tree volumes. Part I: computer vision and pattern recognition; machine learning and applications; image processing. Part II: healthcare and medical imaging; biometrics and applications. Part III: document image analysis; image analysis in agriculture; data mining, information retrieval and applications.
Автор: Henry Braun, Pavan Turaga, Andreas Spanias, Sameeksha Katoch, Suren Jayasuriya, Cihan Tepedelenlioglu Название: Reconstruction-Free Compressive Vision for Surveillance Applications ISBN: 1681735547 ISBN-13(EAN): 9781681735542 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 6930.00 р. Наличие на складе: Нет в наличии.
Описание: Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.
Описание: Comprising papers on diverse aspects of bio-inspired models, soft computing and hybrid intelligent systems, the articles in this book are divided into four main parts that cover important research areas such as fuzzy and bio-inspired problem-solving models.
Описание: This book proposes a semi-discrete version of the theory of Petitot and Citti-Sarti, leading to a left-invariant structure over the group SE(2,N), restricted to a finite number of rotations.
Автор: James C. Bezdek; James Keller; Raghu Krisnapuram; Название: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing ISBN: 0792385217 ISBN-13(EAN): 9780792385219 Издательство: Springer Рейтинг: Цена: 62750.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This study introduces the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision.
Описание: The papers are organized in topical sections on mathematical theory of PR, supervised and unsupervised classification, feature or instance selection for classification, image analysis and retrieval, signals analysis and processing, applications of pattern recognition, biometrics, video analysis, and data mining.
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