Structural, Syntactic, and Statistical Pattern Recognition (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
866
Utgivningsdatum
2002-07-01
Upplaga
2002 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Medarbetare
Caelli, Terry (ed.), Amin, Adnan (ed.), Duin, Robert P. W. (ed.), Kamel, Mohamed (ed.), Ridder, Dick de (ed.)
Illustratör/Fotograf
maps Illustrations
Illustrationer
XXXII, 866 p.
Dimensioner
234 x 156 x 44 mm
Vikt
1212 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783540440116
Structural, Syntactic, and Statistical Pattern Recognition (häftad)

Structural, Syntactic, and Statistical Pattern Recognition

Joint IAPR International Workshops SSPR 2002 and SPR 2002, Windsor, Ontario, Canada, August 6-9, 2002. Proceedings

Häftad Engelska, 2002-07-01
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This volume contains all papers presented at SSPR 2002 and SPR 2002 hosted by the University of Windsor, Windsor, Ontario, Canada, August 6-9, 2002. This was the third time these two workshops were held back-to-back. SSPR was the ninth International Workshop on Structural and Syntactic Pattern Recognition and the SPR was the fourth International Workshop on Statis- cal Techniques in Pattern Recognition. These workshops have traditionally been held in conjunction with ICPR (International Conference on Pattern Recog- tion), and are the major events for technical committees TC2 and TC1, resp- tively, of the International Association of Pattern Recognition (IAPR). The workshops were held in parallel and closely coordinated. This was an attempt to resolve the dilemma of how to deal, in the light of the progressive specialization of pattern recognition, with the need for narrow-focus workshops without further fragmenting the ?eld and introducing yet another conference that would compete for the time and resources of potential participants. A total of 116 papers were received from many countries with the submission and reviewingprocesses beingcarried out separately for each workshop. A total of 45 papers were accepted for oral presentation and 35 for posters. In addition four invited speakers presented informative talks and overviews of their research. They were: Tom Dietterich, Oregon State University, USA Sven Dickinson, the University of Toronto, Canada Edwin Hancock, University of York, UK Anil Jain, Michigan State University, USA SSPR 2002 and SPR 2002 were sponsored by the IAPR and the University of Windsor.
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Invited Talks.- Spectral Methods for View-Based 3-D Object Recognition Using Silhouettes.- Machine Learning for Sequential Data: A Review.- Graph-Based Methods for Vision: A Yorkist Manifesto.- SSPR.- Reducing the Computational Cost of Computing Approximated Median Strings.- Tree k-Grammar Models for Natural Language Modelling and Parsing.- Algorithms for Learning Function Distinguishable Regular Languages.- Non-bayesian Graph Matching without Explicit Compatibility Calculations.- Spectral Feature Vectors for Graph Clustering.- Identification of Diatoms by Grid Graph Matching.- String Edit Distance, Random Walks and Graph Matching.- Learning Structural Variations in Shock Trees.- A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs.- Inexact Multisubgraph Matching Using Graph Eigenspace and Clustering Models.- Optimal Lower Bound for Generalized Median Problems in Metric Space.- Structural Description to Recognising Arabie Characters Using Decision Tree Learning Techniques.- Feature Approach for Printed Document Image Analysis.- Example-Driven Graphics Recognition.- Estimation of Texels for Regular Mosaics Using Model-Based Interaction Maps.- Using Graph Search Techniques for Contextual Colour Retrieval.- Comparing Shape and Temporal PDMs.- Linear Shape Recognition with Mixtures of Point Distribution Models.- Curvature Weighted Evidence Combination for Shape-from-Shading.- Probabilistic Decisions in Production Net An Example from Vehicle Recognition.- Hierarchical Top Down Enhancements of Robust PCA.- An Application of Machine Learning Techniques for the Classification of Glaucomatous Progression.- Poster Papers.- Estimating the Joint Probability Distribution of Random Vertices and Arcs by Means of Second-Order Random Graphs.- Successive Projection Graph Matching.- Compact Graph Model of Handwritten Images: Integration into Authentification and Recognition.- A Statistical and Structural Approach for Symbol Recognition, Using XML Modelling.- A New Algorithm for Graph Matching with Application to Content-Based Image Retrieval.- Efficient Computation of 3-D Moments in Terms of an Object's Partition.- A Visual Attention Operator Based on Morphological Models of Images and Maximum Likelihood Decision.- Disparity Using Feature Points in Multi Scale.- Detecting Perceptually Important Regions in an Image Based on Human Visual Attention Characteristic.- Development of Spoken Language User Interfaces: A Tool Kit Approach.- Document Image De-warping for Text/Graphics Recognition.- A Complete OCR System for Gurmukhi Script.- Texprint: A New Algorithm to Discriminate Textures Structurally.- Optical Music Interpretation.- On the Segmentation of Color Cartographic Images.- SPR.- Projection Pursuit Fitting Gaussian Mixture Models.- Asymmetric Gaussian and Its Application to Pattern Recognition.- Modified Predictive Validation Test for Gaussian Mixture Modelling.- Performance Analysis and Comparison of Linear Combiners for Classifier Fusion.- Comparison of Two Classification Methodologies on a Real-World Biomedical Problem.- Evidence Accumulation Clustering Based on the K-Means Algorithm.- A Kernel Approach to Metric Multidimensional Scaling.- On Feature Selection with Measurement Cost and Grouped Features.- Classifier-Independent Feature Selection Based on Non-parametric Discriminant Analysis.- Effects of Many Feature Candidates in Feature Selection and Classification.- Spatial Representation of Dissimilarity Data via Lower-Complexity Linear and Nonlinear Mappings.- A Method to Estimate the True Mahalanobis Distance from Eigenvectors of Sample Covariance Matrix.- Non-iterative Heteroscedastic Linear Dimension Reduction for Two-Class Data.- Some Experiments in Supervised Pattern Recognition with Incomplete Training Samples.- Recursive Prototype Reduction Schemes Applicable for Large Data Sets.- Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition.- Training Set Expansion in H