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1.

電子ブック

EB
Sameer Singh, Rama Chellappa, S. Z. Li, Massimo Tistarelli
出版情報: Springer eBooks Computer Science , Springer London, 2009
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2.

電子ブック

EB
Nanning Zheng, Sameer Singh, Jianru Xue
出版情報: Springer eBooks Computer Science , Springer London, 2009
所蔵情報: loading…
目次情報: 続きを見る
Pattern Analysis and Statistical Learning
Introduction / 1.1:
Statistical Pattern Recognition / 1.1.1:
Pattern Theory / 1.1.2:
Statistical Classification / 1.2:
Feature Extraction and Selection / 1.2.1:
Classifier / 1.2.2:
Visual Pattern Representation / 1.3:
The Curse of Dimensionality / 1.3.1:
Dimensionality Reduction Techniques / 1.3.2:
Statistical Learning / 1.4:
Prediction Risk / 1.4.1:
Supervised, Unsupervised, and Others / 1.4.2:
Summary / 1.5:
References
Unsupervised Learning for Visual Pattern Analysis / 2:
Unsupervised Learning / 2.1:
Visual Pattern Analysis / 2.1.2:
Outline / 2.1.3:
Cluster Analysis / 2.2:
Clustering Algorithms / 2.3:
Partitional Clustering / 2.3.1:
Hierarchical Clustering / 2.3.2:
Perceptual Grouping / 2.4:
Hierarchical Perceptual Grouping / 2.4.1:
Gestalt Grouping Principles / 2.4.2:
Contour Grouping / 2.4.3:
Region Grouping / 2.4.4:
Learning Representational Models for Visual Patterns / 2.5:
Appendix / 2.6:
Component Analysis / 3:
Overview of Component Analysis / 3.1:
Generative Models / 3.3:
Principal Component Analysis / 3.3.1:
Nonnegative Matrix Factorization / 3.3.2:
Independent Component Analysis / 3.3.3:
Discriminative Models / 3.4:
Linear Discriminative Analysis / 3.4.1:
Oriented Component Analysis / 3.4.2:
Canonical Correlation Analysis / 3.4.3:
Relevant Component Analysis / 3.4.4:
Standard Extensions of the Linear Model / 3.5:
Latent Variable Analysis / 3.5.1:
Kernel Method / 3.5.2:
Manifold Learning / 3.6:
Mathematical Preliminaries / 4.1:
Manifold Related Terminologies / 4.2.1:
Graph Related Terminologies / 4.2.2:
Global Methods / 4.3:
Multidimensional Scaling / 4.3.1:
Isometric Feature Mapping / 4.3.2:
Variants of the Isomap / 4.3.3:
Local Methods / 4.4:
Locally Linear Embedding / 4.4.1:
Laplacian Eigenmaps / 4.4.2:
Hessian Eigenmaps / 4.4.3:
Diffusion Maps / 4.4.4:
Hybrid Methods: Global Alignment of Local Models / 4.5:
Global Coordination of Local Linear Models / 4.5.1:
Charting a Manifold / 4.5.2:
Local Tangent Space Alignment / 4.5.3:
Functional Approximation / 4.6:
Modeling and Approximating the Visual Data / 5.1:
On Statistical Analysis / 5.2.1:
On Harmonic Analysis / 5.2.2:
Issues of Approximation and Compression / 5.2.3:
Wavelet Transform and Lifting Scheme / 5.3:
Wavelet Transform / 5.3.1:
Constructing a Wavelet Filter Bank / 5.3.2:
Lifting Scheme / 5.3.3:
Lifting-Based Integer Wavelet Transform / 5.3.4:
Optimal Integer Wavelet Transform / 5.4:
Introducing Adaptability into the Wavelet Transform / 5.5:
Curve Singularities in an Image / 5.5.1:
Anisotropic Basis / 5.5.2:
Adaptive Lifting-Based Wavelet / 5.5.3:
Adaptive Lifting Structure / 5.6:
Adaptive Prediction Filters / 5.6.1:
Adaptive Update Filters / 5.6.2:
Adaptive Directional Lifting Scheme / 5.7:
ADL Framework / 5.7.1:
Implementation of ADL / 5.7.2:
Motion Compensation Temporal Filtering in Video Coding / 5.8:
Overview of MCTF / 5.8.1:
MC in MCTF / 5.8.2:
Adaptive Lifting-Based Wavelets in MCTF / 5.8.3:
Summary and Discussions / 5.9:
Supervised Learning for Visual Pattern Classification / 6:
An Example of Supervised Learning / 6.1:
Support Vector Machine / 6.3:
Optimal Separating Hyper-plane / 6.3.1:
Realization of SVM / 6.3.2:
Kernel Function / 6.3.3:
Boosting Algorithm / 6.4:
AdaBoost Algorithm / 6.4.1:
Theoretical Analysis of AdaBoost / 6.4.2:
AdaBoost Algorithm as an Additive Model / 6.4.3:
Statistical Motion Analysis / 7:
Problem Formulation / 7.1:
Overview of Computing Techniques / 7.1.2:
Bayesian Estimation of Optical Flow / 7.2:
MAP Estimation / 7.2.1:
Occlusion / 7.2.3:
Model-Based Motion Analysis / 7.3:
Motion Models / 7.3.1:
Statistical Model Selection / 7.3.2:
Learning Parameterized Models / 7.3.3:
Motion Segmentation / 7.4:
Layered Model: Multiple Motion Models / 7.4.1:
Clustering Optical Flow Field into Layers / 7.4.2:
Mixture Estimation for Layer Extraction / 7.4.3:
Statistics of Optical Flow / 7.5:
Motion Prior Modeling / 7.5.1:
Contrastive Divergence Learning / 7.5.3:
Bayesian Tracking of Visual Objects / 7.6:
Sequential Bayesian Estimation / 8.1:
Problem Formulation of Bayesian Tracking / 8.2.1:
Kalman Filter / 8.2.2:
Grid-Based Methods / 8.2.3:
Sub-optimal Filter / 8.2.4:
Monte Carlo Filtering / 8.3:
Sequential Importance Sampling / 8.3.1:
Sequential Monte Carlo Filtering / 8.3.3:
Particle Filter / 8.3.4:
Object Representation Model / 8.4:
Visual Learning for Object Representation / 8.4.1:
Active Contour / 8.4.2:
Appearance Model / 8.4.3:
Probabilistic Data Fusion for Robust Visual Tracking / 8.5:
Earlier Work on Robust Visual Tracking / 9.1:
Data Fusion-Based Visual Tracker / 9.3:
Sequential Bayesian Estimator / 9.3.1:
The Four-Layer Data Fusion Visual Tracker / 9.3.2:
Layer 1: Visual Cue Fusion / 9.4:
Fusion Rules: Product Versus Weighted Sum / 9.4.1:
Adaptive Fusion Rule / 9.4.2:
Online Approach to Determining the Reliability of a Visual cue / 9.4.3:
Layer 2: Model Fusion / 9.5:
Pseudo-Measurement-Based Multiple Model Method / 9.5.1:
Likelihood Function / 9.5.2:
Layer 3: Tracker Fusion / 9.6:
Interactive Multiple Trackers / 9.6.1:
Practical Issues / 9.6.3:
Sensor Fusion / 9.7:
Implementation Issues and Empirical Results / 9.8:
Visual Cue Fusion Layer / 9.8.1:
Model Fusion Layer / 9.8.2:
Tracker Fusion Layer / 9.8.3:
Bottom-Up Fusion with a Three-Layer Structure / 9.8.4:
Multi-Censor Fusion Tracking System Validation / 9.8.5:
Multitarget Tracking in Video-Part I / 9.9:
Overview of MTTV Methods / 10.1:
Static Model for Multitarget / 10.3:
Problem formulation / 10.3.1:
Observation Likelihood Function / 10.3.2:
Prior Model / 10.3.3:
Approximate Inference / 10.4:
Model Approximation / 10.4.1:
Algorithm Approximation / 10.4.2:
Fusing Information from Temporal and Bottom-Up Detectors / 10.5:
Experiments and Discussions / 10.6:
Proof-of-Concept / 10.6.1:
Comparison with Other Trackers / 10.6.2:
The Efficiency of the Gibbs Sampler / 10.6.3:
Multi-Target Tracking in Video - Part II / 10.7:
Overview of the MTTV Data Association Mechanism / 11.1:
Handing Data Association Explicitly / 11.2.1:
Handing Data Association Implicitly / 11.2.2:
Detection and Tracking / 11.2.3:
The Generative Model for MTT / 11.3:
The Generative Model / 11.3.1:
Approximating The Marginal Term / 11.4:
The State Prediction / 11.4.1:
Existence and Association Posterior / 11.4.2:
Approximating the Interactive Term / 11.5:
Hybrid Measurement Process / 11.6:
Experiments and Discussion / 11.7:
Tracking Soccer Players / 11.7.1:
Tracking Pedestrians in a Dynamic Scene / 11.7.2:
Discussion / 11.7.3:
Information Processing in Cognition Process and New Artificial Intelligent Systems / 11.8:
Cognitive Model: A Prototype of Intelligent System / 12.1:
Issues in Theories and Methodologies of Current Brain Research and Vision Science / 12.3:
Interactive Behaviors and Selective Attention in the Process of Visual Cognition / 12.4:
Intelligent Information Processing and Modeling Based on Cognitive Mechanisms / 12.5:
Cognitive Modeling and Behavioral Control in Complex Systems in an Information Environment / 12.5.1:
Distributed Cognition / 12.5.2:
Neurophysiological Mechanism of Learning and Memory and Information Processing Model / 12.5.3:
Cognitive Neurosciences and Computational Neuroscience / 12.6:
Consciousness and Intention Reading / 12.6.1:
The Core of Computational Neuroscience is to Compute and Interpret the States of Nervous System / 12.6.2:
Soft Computing Method / 12.7:
Index / 12.8:
Pattern Analysis and Statistical Learning
Introduction / 1.1:
Statistical Pattern Recognition / 1.1.1:
3.

電子ブック

EB
Sameer Singh, Riad I. Hammoud
出版情報: Springer eBooks Computer Science , Springer London, 2009
所蔵情報: loading…
目次情報: 続きを見る
Preface
Ordinary and partial transformations / 1:
The semigroups Tn, PT n and ISn / 2:
Generating Systems / 3:
Ideals and Green's relations / 4:
Subgroups and subsemigroups / 5:
Other relations on semigroups / 6:
Endomorphisms / 7:
Nilpotent subsemigroups / 8:
Presentation / 9:
Transitive actions / 10:
Linear representations / 11:
Cross-sections / 12:
Variants / 13:
Order-related subsemigroups / 14:
Answers and hints to exercises
Bibliography
List of notation
Index
Preface
Ordinary and partial transformations / 1:
The semigroups Tn, PT n and ISn / 2:
4.

電子ブック

EB
Sameer Singh, Branislav Kisacanin
出版情報: Springer eBooks Computer Science , Springer London, 2009
所蔵情報: loading…
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