Konstantinos N. Plataniotis - Böcker

Edited by a panel of experts, this book fills a gap in the existing literature by comprehensively covering system, processing, and application aspects of biometrics, based on a wide variety of biometric traits. The book provides an extensive survey of biometrics theory, methods,and applications, making it an indispensable source of information for researchers, security experts, policy makers, engineers, practitioners, and graduate students. The book's wide and in-depth coverage of biometrics enables readers to build a strong, fundamental understanding of theory and methods, and provides a foundation for solutions to many of today’s most interesting and challenging biometric problems. Biometric traits covered:Face, Fingerprint, Iris, Gait, Hand Geometry, Signature, Electrocardiogram (ECG), Electroencephalogram (EEG), physiological biometrics.Theory, Methods and Applications covered:Multilinear Discriminant Analysis, Neural Networks for biometrics, classifier design, biometric fusion, Event-Related Potentials, person-specific characteristic feature selection, image and video-based face, recognition/verification, near-infrared face recognition, elastic graph matching, super-resolution of facial images, multimodal solutions, 3D approaches to biometrics, facial aging models for recognition, information theory approaches to biometrics, biologically-inspired methods, biometric encryption, decision-making support in biometric systems, privacy in biometrics.

Describing the relevant detection and estimation theory, this detailed guide provides the background knowledge needed to tackle the design of practical WLAN positioning systems. It sets out key system-level challenges and design considerations in increasing positioning accuracy and reducing computational complexity, and it also examines design trade-offs and experimental results. Radio characteristics in real environments are discussed, as are the theoretical aspects of non-parametric statistical tools appropriate for modeling radio signals, statistical estimation techniques and the model-based stochastic estimators often used for positioning. A historical account of positioning systems in also included, giving graduate students, researchers and practitioners alike the perspective needed to understand the benefits and potential applications of WLAN positioning.

Color Image Processing: Methods and Applications embraces two decades of extraordinary growth in the technologies and applications for color image processing. The book offers comprehensive coverage of state-of-the-art systems, processing techniques, and emerging applications of digital color imaging.To elucidate the significant progress in specialized areas, the editors invited renowned authorities to address specific research challenges and recent trends in their area of expertise. The book begins by focusing on color fundamentals, including color management, gamut mapping, and color constancy. The remaining chapters detail the latest techniques and approaches to contemporary and traditional color image processing and analysis for a broad spectrum of sophisticated applications, including:Vector and semantic processingSecure imagingObject recognition and feature detectionFacial and retinal image analysisDigital camera image processingSpectral and superresolution imagingImage and video colorizationVirtual restoration of artworkVideo shot segmentation and surveillanceColor Image Processing: Methods and Applications is a versatile resource that can be used as a graduate textbook or as stand-alone reference for the design and the implementation of various image and video processing tasks for cutting-edge applications.This book is part of the Digital Imaging and Computer Vision series.

Due to advances in sensor, storage, and networking technologies, data is being generated on a daily basis at an ever-increasing pace in a wide range of applications, including cloud computing, mobile Internet, and medical imaging. This large multidimensional data requires more efficient dimensionality reduction schemes than the traditional techniques. Addressing this need, multilinear subspace learning (MSL) reduces the dimensionality of big data directly from its natural multidimensional representation, a tensor.Multilinear Subspace Learning: Dimensionality Reduction of Multidimensional Data gives a comprehensive introduction to both theoretical and practical aspects of MSL for the dimensionality reduction of multidimensional data based on tensors. It covers the fundamentals, algorithms, and applications of MSL.Emphasizing essential concepts and system-level perspectives, the authors provide a foundation for solving many of today’s most interesting and challenging problems in big multidimensional data processing. They trace the history of MSL, detail recent advances, and explore future developments and emerging applications.The book follows a unifying MSL framework formulation to systematically derive representative MSL algorithms. It describes various applications of the algorithms, along with their pseudocode. Implementation tips help practitioners in further development, evaluation, and application. The book also provides researchers with useful theoretical information on big multidimensional data in machine learning and pattern recognition. MATLAB® source code, data, and other materials are available at www.comp.hkbu.edu.hk/~haiping/MSL.html

Reporting the state of the art of color image processing, this monograph fills an existing gap in the literature on digital signal and image processing. It can serve the needs of different users at different levels: as a textbook which covers a graduate image processing course, as a up-to-date reference for researchers since it offers a broad survey of the relevant literature, and as a relevant information source for development engineers who work in the design and the implementation of various image processing tasks. Part of the material in the book was the basis of seminars at the University of Toronto. The book contains numerous examples and pictures of color image processing results, as well as tables which summarize the results of the analysis. Algorithms implemented in JAVA can be downloaded from the author's website .

Reporting the state of the art of color image processing, this monograph fills an existing gap in the literature on digital signal and image processing. It can serve the needs of different users at different levels: as a textbook which covers a graduate image processing course, as a up-to-date reference for researchers since it offers a broad survey of the relevant literature, and as a relevant information source for development engineers who work in the design and the implementation of various image processing tasks. Part of the material in the book was the basis of seminars at the University of Toronto. The book contains numerous examples and pictures of color image processing results, as well as tables which summarize the results of the analysis. Algorithms implemented in JAVA can be downloaded from the author's website .