结构、句法和统计的图形认知/ Structural, syntactic, and statistical pattern recognition
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作者： Dit-Yan Yeung等著
出 版 社： 湖北辞书出版社
出版时间： 2006-12-1字数：版次： 1页数： 939印刷时间： 2006/12/01开本：印次：纸张： 胶版纸I S B N ： 9783540372363包装： 平装编辑推荐
The LNCS series reports state-of-the-art results in computer science research，development，and education，at a high level and in both printed and electronic form.Enjoying tight cooperation with the R&D community，with numerous individuals，as well as with prestigious organizations and societies，LNCS has grown into the most comprehensive computer science research forum available.
The scope of LNCS including its subseries LNAI spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields.The type of material published traditionally includes.
—proceedings （published in time for the respective conference）
—post-proceedings （consisting of thoroughly revised final full papers）
—research monographs（which may be based on outstanding PhD work，research projects，technical reports，etc.）
内容简介
This book constitutes the refereed proceedings of the 11th International Workshop on Structural and Syntactic Pattern Recognition, SSPR 2006 and the 6th International Workshop on Statistical Techniques in Pattern Recognition, SPR 2006, held jointly in Hong Kong, China in August 2006 as a satellite event of the 18th International Conference of Pattern Recognition, ICPR 2006.
The 38 revised full papers and 61 revised poster papers presented together with 4 invited papers were carefully reviewed and selected from 217 submissions. The papers are organized in topical sections on image analysis, vision, character recognition, bayesian networks, graph-based methods, similarity and feature extraction, image and video, vision, kernel-based methods, recognition and classification, similarity and feature extraction, document analysis, graph-based methods, recognition and classification, image analysis, facial image analysis, representation, feature selection, clustering, multiple classifier systems, recognition and classification, unsupervised learning, dimensionality, representation, biometrics, and applications.
目录
Invited Talks
SSPR
Image Analysis
Vision
Character Recognition
Bayesian Networks
Graph-Based Methods
Similarity and Feature Extraction
Poster Papers
Image and Video
Vision
Kernel-Based Methods
Recognition and Callification
Similarity and Feature Extraction
Document Analysis
Graph-Based Methods
SPR
Recognition and Classification 1
Image Analysis
Facial Image Analysis
Recognition and Classfication
Representation
Feature Selection
Clustering
Facial Image Analysis
Poster Papers
Multiple Callsifier Systems
Recognition and Calasificaition
Unsupervised Learning
Dimensionality
Repressentation
Biometrics
Applications
Author Index
