《模式識別、機器智慧型與生物特徵識別》是2011年高等教育出版社出版的圖書，作者是王申培。本書介紹了廣泛套用的人工智慧技術——模式識別及其套用的最新進展。
基本介紹
- 書名：模式識別、機器智慧型與生物特徵識別
- 作者：（美國）王申培
- 原版名稱： Pattern Recognition,Machine Intelligence and Biometrics
- ISBN：9787040331394
- 頁數：866頁
- 出版社：高等教育出版社
- 出版時間：2011年7月1日
- 裝幀：精裝
- 開本：16
- 正文語種：英語
內容簡介,目錄,
內容簡介
《模式識別、機器智慧型與生物特徵識別(英文版)》收集了世界一流的模式識別、人工智慧和生物特徵識別技術領域專家編寫的31章內容，涵蓋模式識別與機器智慧型、計算機視覺與圖像處理、人臉識別與取證、生物特徵身份驗證等多方面結合的研究。其套用跨越多個領域，從工程、科學研究和實驗，到生物醫學和醫學診斷，再到身份認證和國土安全。此外，《模式識別、機器智慧型與生物特徵識別(英文版)》還介紹了人類行為的計算機建模和仿真。
《模式識別、機器智慧型與生物特徵識別(英文版)》是計算機與信息類以及通信與控制類專業研究生和相關研究人員的必備參考書。
P0trckS.P.Wang（王申培）美國東北大學教授，上海華東師大紫江學者，台灣科技大學客座教授。
目錄
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 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
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 forReal-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
……
Part Ⅲ：Face Recognition and Forensics
PartⅣ：Biometric Authentication
