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Pattern Recognition, Machine Intelligence and Biometrics （模式识别、机器智能与生物特征识别，英文版）

样章
作者：
Patrick S.P. Wang
定价：
138.00元
ISBN：
978-7-04-033139-4
版面字数：
1416.000千字
开本：
16开
全书页数：
866页
装帧形式：
精装
重点项目：
暂无
出版时间：
2011-07-29
物料号：
33139-00
读者对象：
学术著作
一级分类：
自然科学
二级分类：
信息与通信工程
三级分类：
信号与信息处理

《模式识别、机器智能与生物特征识别（英文版）》介绍广泛应用的人工智能技术——模式识别及其应用的最新进展，收集了世界一流的模式识别、人工智能和生物 特征识别技术领域专家编写的31章内容，涵盖模式识别与机器智能、计算机视觉与图像处理、人脸识别与取证、生物特征身份验证等多方面结合的研究。其应用跨 越多个领域，从工程、科学研究和实验，到生物医学和医学诊断，再到身份认证和国土安全。此外，《模式识别、机器智能与生物特征识别（英文版）》还介绍了人 类行为的计算机建模和仿真。

《模式识别、机器智能与生物特征识别（英文版）》是计算机与信息类以及通信与控制类专业研究生和相关研究人员的必备参考书。

  • 前辅文
  • Part I: Pattern Recognition and Machine Intelligence
    • 1 A Review of Applications of Evolutionary Algorithms in Pattern Recognition
      • 1.1 Introduction
      • 1.2 Basic Notions of Evolutionary Algorithms
      • 1.3 A Review of EAs in Pattern Recognition
      • 1.4 Future Research Directions
      • 1.5 Conclusions
      • References
    • 2 Pattern Discovery and Recognition in Sequences
      • 2.1 Introduction
      • 2.2 Sequence Patterns and Pattern Discovery-A Brief Review
      • 2.3 Our Pattern Discovery Framework
      • 2.4 Conclusion
      • References
    • 3 A Hybrid Method of Tone Assessment for Mandarin CALL System
      • 3.1 Introduction
      • 3.2 Related Work
      • 3.3 Proposed Approach
      • 3.4 Experimental Procedure and Analysis
      • 3.5 Conclusions
      • References
    • 4 Fusion with Infrared Images for an Improved Performance and Perception
      • 4.1 Introduction
      • 4.2 The Principle of Infrared Imaging
      • 4.3 Fusion with Infrared Images
      • 4.4 Applications
      • 4.5 Summary
      • References
    • 5 Feature Selection and Ranking for Pattern Classification in Wireless Sensor Networks
      • 5.1 Introduction
      • 5.2 General Approach
      • 5.3 Sensor Ranking
      • 5.4 Experiments
      • 5.5 Summary, Discussion and Conclusions
      • References
    • 6 Principles and Applications of RIDED-2D-A Robust Edge Detection Method in Range Images
      • 6.1 Introduction
      • 6.2 Definitions and Analysis
      • 6.3 Principles of Instantaneous Denoising and Edge Detection
      • 6.4 Experiments and Evaluations
      • 6.5 Discussions and Applications
      • 6.6 Conclusions and Prospects
      • References
  • Part II: Computer Vision and Image Processing
    • 7 Lens Shading Correction for Dirt Detection
      • 7.1 Introduction
      • 7.2 Background
      • 7.3 Our Proposed Method
      • 7.4 Experimental Results
      • 7.5 Conclusions
      • References
    • 8 Using Prototype-Based Classification for Automatic Knowledge Acquisition
      • 8.1 Introduction
      • 8.2 Prototype-Based Classification
      • 8.3 Methodology
      • 8.4 Application
      • 8.5 Results
      • 8.6 Conclusion
      • References
    • 9 Tracking Deformable Objects with Evolving Templates for Real-Time Machine Vision
      • 9.1 Introduction
      • 9.2 Problem Formulation
      • 9.3 Search Framework for Computing Template Position
      • 9.4 Updating Framework for Computing Template Changes
      • 9.5 Multiple Object Tracking and Intensity Information
      • 9.6 Experiments and Results
      • 9.7 Conclusions and Future Work
      • References
    • 10 Human Extremity Detection for Action Recognition
      • 10.1 Introduction
      • 10.2 Relevant Works
      • 10.3 Extremities as Points on a Contour
      • 10.4 Extremities as Image Patches
      • 10.5 Experimental Results
      • 10.6 Conclusion
      • References
    • 11 Ensemble Learning for Object Recognition and Tracking
      • 11.1 Introduction
      • 11.2 Random Subspace Method
      • 11.3 Boosting Method
      • References
    • 12 Depth Image Based Rendering
      • 12.1 Introduction
      • 12.2 Depth Image Based Rendering
      • 12.3 Disocclusions
      • 12.4 Other Challenges
      • 12.5 Conclusion
      • References
  • Part III: Face Recognition and Forensics
    • 13 Gender and Race Identification by Man and Machine
      • 13.1 Introduction
      • 13.2 Background
      • 13.3 Silhouetted Profile Faces
      • 13.4 Frontal Faces
      • 13.5 Fusing the Frontal View and Silhouetted Profile View Classifiers
      • 13.6 Human Experiments
      • 13.7 Observations and Discussion
      • 13.8 Concluding Remarks
      • References
    • 14 Common Vector Based Face Recognition Algorithm
      • 14.1 Introduction
      • 14.2 Algorithm Description
      • 14.3 Two Methods Based on Common Vector
      • 14.4 Experiments and Results
      • 14.5 Conclusion and Future Research
      • References
    • 15 A Look at Eye Detection for Unconstrained Environments
      • 15.1 Introduction
      • 15.2 Related Work
      • 15.3 Machine Learning Approach
      • 15.4 Correlation Filter Approach
      • 15.5 Experiments
      • 15.6 Conclusions
      • References
    • 16 Kernel Methods for Facial Image Preprocessing
      • 16.1 Introduction
      • 16.2 Kernel PCA
      • 16.3 Kernel Methods for Nonlinear Image Preprocessing
      • 16.4 Face Image Preprocessing Using KPCA
      • 16.5 Summary
      • References
    • 17 Fingerprint Identification-Ideas, Influences, and Trends of New Age
      • 17.1 Introduction
      • 17.2 System Architecture and Applications of Fingerprint Matching
      • 17.3 The Early Years
      • 17.4 Recent Feature Extraction Techniques-Addressing Core Problem
      • 17.5 Conclusion and Future Directions
      • References
    • 18 Subspaces Versus Submanifolds-A Comparative Study of Face Recognition
      • 18.1 Introduction
      • 18.2 Notation and Definitions
      • 18.3 Brief Review of Subspace-Based Face Recognition
      • Algorithms
      • 18.4 Submanifold-Based Algorithms for Face Recognition
      • 18.5 Experiments Results and Analysis
      • 18.6 Conclusion
      • References
    • 19 Linear and Nonlinear Feature Extraction Approaches for Face Recognition
      • 19.1 Introduction
      • 19.2 Linear Feature Extraction Methods
      • 19.3 Non-Linear Feature Extraction Methods
      • 19.4 Conclusions
      • References
    • 20 Facial Occlusion Reconstruction Using Direct Combined Model
      • 20.1 Introduction
      • 20.2 Direct Combined Model Algorithm
      • 20.3 Reconstruction System
      • 20.4 Experimental Results
      • 20.5 Conclusions
      • References
    • 21 Generative Models and Probability Evaluation for Forensic Evidence
      • 21.1 Introduction
      • 21.2 Generative Models of Individuality
      • 21.3 Application to Birthdays
      • 21.4 Application to Human Heights
      • 21.5 Application to Fingerprints
      • 21.6 Summary
      • References
    • 22 Feature Mining and Pattern Recognition in Multimedia Forensics-Detection of JPEG Image Based Steganography, Double-Compression, Interpolations and WAV Audio Based Steganography
      • 22.1 Introduction.
      • 22.2 Related Works
      • 22.3 Statistical Characteristics and Modification
      • 22.4 Feature Mining for JPEG Image Forensics
      • 22.5 Derivative Based Audio Steganalysis
      • 22.6 Pattern Recognition Techniques
      • 22.7 Experiments
      • 22.8 Conclusions
      • References
      • Part IV: Biometric Authentication
    • 23 Biometric Authentication
      • 23.1 Introduction
      • 23.2 Basic Operations of a Biometric System
      • 23.3 Biometrics Standardization
      • 23.4 Certification of Biometric System
      • 23.5 Cloud Service—Web Service Authentication
      • 23.6 Challenges of Large Scale Deployment of Biometric Systems
      • 23.7 Conclusion
      • References
    • 24 Radical-Based Hybrid Statistical-Structural Approach for Online Handwritten Chinese Character Recognition
      • 24.1 Introduction
      • 24.2 Overview of Radical-Based Approach
      • 24.3 Formation of Radical Models
      • 24.4 Radical-Based Recognition Framework
      • 24.5 Experiments
      • 24.6 Concluding Remarks
      • References
    • 25 Current Trends in Multimodal Biometric System—Rank Level Fusion
      • 25.1 Introduction
      • 25.2 Multimodal Biometric System
      • 25.3 Fusion in Multimodal Biometric System
      • 25.4 Rank Level Fusion
      • 25.5 Conclusion
      • References
    • 26 Off-line Signature Verification by Matching with a 3D Reference Knowledge Image—From Research to Actual Application
      • 26.1 Introduction
      • 26.2 Used Signature Data
      • 26.3 Image Types Used for Feature Extraction and Evaluation
      • 26.4 Skills of Forgery Creation of Used Forgeries
      • 26.5 Previous Work and Motivation for 3D RKI
      • 26.6 3D Reference Knowledge of Signature
      • 26.7 Ammar Matching Technique
      • 26.8 Feature Extraction
      • 26.9 Distance Measure and Verification
      • 26.10 Experimental Results and Discussion
      • 26.11 Limited Results are Shown and Discussed
      • 26.12 AMT Features and Signature Recognition
      • 26.13 AMT and Closely Related Works
      • 26.14 Transition from Research to Prototyping then Pilot Project and Actual Use
      • 26.15 Conclusions
      • References
    • 27 Unified Entropy Theory and Maximum Discrimination on Pattern Recognition
      • 27.1 Introduction
      • 27.2 Unified Entropy Theory in Pattern Recognition
      • 27.3 Mutual-Information—Discriminate Entropy in Pattern Recognition
      • 27.4 Mutual Information Discrimination Analysis in Pattern Recognition
      • 27.5 Maximum MI principle
      • 27.6 Maximum MI Discriminate SubSpace Recognition in Handwritten Chinese Character Recognition
      • 27.7 Conclusion
      • References
    • 28 Fundamentals of Biometrics—Hand Written Signature and Iris
      • 28.1 Prologue
      • 28.2 Fundamentals of Handwritten Signature
      • 28.3 Acquisition
      • 28.4 Databases
      • 28.5 Signature Analysers
      • 28.6 Off-line Methods
      • 28.7 On-line Methods
      • 28.8 Fundamentals of Iris
      • 28.9 Feature Extraction
      • 28.10 Preprocessing
      • 28.11 Iris Image Databases
      • 28.12 Iris Analyzers
      • 28.13 Conclusion
      • References
    • 29 Recent Trends in Iris Recognition
      • 29.1 Introduction
      • 29.2 Basic Modules of Iris Recognition
      • 29.3 Performance Measures
      • 29.4 Limitations of Current Techniques
      • 29.5 Future Scope
      • References
    • 30 Using Multisets of Features and Interactive Feature Selection to Get Best Qualitative Performance for Automatic Signature Verification
      • 30.1 Introduction
      • 30.2 Signature Data
      • 30.3 ASV Systems Using Threshold-Based Decision
      • 30.4 MSF and Its Performance
      • 30.5 IFS and QP
      • 30.6 Conclusion
      • References
    • 31 Fourier Transform in Numeral Recognition and Signature Verification
      • 31.1 Concepts of Digital Transforms
      • 31.2 Orthonormal System of Trigonometric Functions
      • 31.3 Introduction to Discrete Fourier Transform
      • 31.4 Properties of DFT
      • 31.5 DFT Calculation Problem
      • 31.6 Description of a Numeral Through Fourier Coefficents
      • 31.7 Numeral Recognition Through Fourier Transform
      • 31.8 Signature Verification Systems Trough Fourier Analysis
      • 31.9 On-line Signature Verification System Based on Fourier Analysis of Strokes
      • References
  • Index

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