David Zhang

Wangmeng Zuo

Naimin Li

The introduction of traditional Chinese medicine (TCM) through modern information technology will not only achieve the objective progress of the heritage of thousands of years of TCM, but also deliver novel discoveries for modern medicines. This book is an advanced monograph based on a decade's worth of research work by the authors. After a brief introduction on the four diagnosis approaches in TCM, this book delves into the three main TCM data analysis techniques: computerized tongue, pulse and odor analysis. Both graduate students and researchers in computerized TCM data analysis will benefit from the book as it will provide a comprehensive understanding of the state-of-the-art analysis methods, image / signal acquisition devices, and the related feature extraction and classification methods. Contents:Diagnosis Methods in Traditional Chinese Medicine:IntroductionComputerized Tongue Image Analysis:Tongue Image Acquisition and PreprocessingAutomated Tongue SegmentationTongue Image Feature AnalysisComputerized Tongue DiagnosisComputerized Pulse Signal Analysis:Pulse Signal Acquisition and PreprocessingFeature Extraction of Pulse SignalsClassification of Pulse SignalsComputerized Odor Signal Analysis:Breath Analysis System: Design and OptimizationFeature Extraction and Classification of Breath Odor Signals Readership: Students and researchers from both academia and industry: in the field of computerized traditional Chinese medicine data analysis, or in the related fields, e.g., medical image/signal analysis, biometrics, and pattern recognition. Key Features:The first monograph on computerized traditional Chinese medicine data analysis (CTDA)In-depth and self-contained introduction on CTDAComprehensive presentation on three main CTDA technologies

Computerized TCM Data Analysis

Medical Biometrics

Yong Xu

This monograph describes the latest advances in discriminative learning methods for biometric recognition. Specifically, it focuses on three representative categories of methods: sparse representation-based classification, metric learning, and discriminative feature representation, together with their applications in palmprint authentication, face recognition and multi-biometrics. The ideas, algorithms, experimental evaluation and underlying rationales are also provided for a better understanding of these methods. Lastly, it discusses several promising research directions in the field of discriminative biometric recognition.

Discriminative Learning in Biometrics

Liang Lin

Dongyu Zhang

Ping Luo

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.

Wangmeng Zuo

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