New Book - Dynamic Vision: From Images to Face Recognition

[Stephen McKenna]

Dear colleagues,

We are pleased to announce the publication of the following book:

"Dynamic Vision: From Images to Face Recognition"
by Shaogang Gong, Stephen J McKenna and Alexandra Psarrou
Imperial College Press (World Scientific Publishing), ISBN 
1-86094-181-8, 344 pp.

Further details are available at 
http://www.computing.dundee.ac.uk/staff/stephen/book.html

The book can be obtained from:
http://www.worldscientific.com/books/bookshop.html or
http://www.amazon.com/exec/obidos/ASIN/1860941818/qid%3D957267900/sr%3D1-2/102-5354817-5263206

Sincerely,

Stephen McKenna
Department of Applied Computing
University of Dundee
DD1 4HN
Tel.: +44 (0)1382 344732    Fax.: +44 (0)1382 345509

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[ Table of contents inserted by Connectionists moderator: ]

Contents

PART I     BACKGROUND 

1 About Face 
The Visual Face, The Changing Face, Computing Faces, Biological Perspectives, The Approach 

2 Perception and Representation 
A Distal Object, Representation by 3D Reconstruction, Two-dimensional
View-based Representation, Image Template-based Representation, The
Correspondence Problem and Alignment, Biological Perspectives,
Discussion

3 Learning Under Uncertainty 
Statistical Learning, Learning as Function Approximation, Bayesian
Inference and MAP Classification, Learning as Density Estimation,
Unsupervised Learning without Density Estimation, Linear
Classification and Regression, Non-linear Classification and
Regression, Adaptation, Biological Perspectives, Discussion

PART II    FROM SENSORY TO MEANINGFUL PERCEPTION 

4 Selective Attention: Where to Look 
Pre-attentive Visual Cues from Motion, Learning Object-based Colour
Cues, Perceptual Grouping for Selective Attention, Data Fusion for
Perceptual Grouping, Temporal Matching and Tracking, Biological
Perspectives, Discussion

5 A Face Model: What to Look For 
Person-independent Face Models for Detection, Modelling the Face
Class, Modelling a Near-face Class, Learning a Decision Boundary,
Perceptual Search, Biological Perspectives, Discussion

6 Understanding Pose 
Feature and Template-based Correspondence, The Face Space across
Views: Pose Manifolds, Template Matching as Affine Transformation,
Similarities to Prototypes across Views, Learning View-based Support
Vector Machines, Biological Perspectives, Discussion

7 Prediction and Adaptation 
Temporal Observations, Propagating First-order Markov Processes,
Kalman Filters, Propagating Non-Gaussian Conditional Densities,
Tracking Attended Regions, Adaptive Colour Models, Selective
Adaptation, Tracking Faces, Pose Tracking, Biological Perspectives,
Discussion

PART III    MODELS OF IDENTITY 

8 Single-View Identification 
Identification Tasks, Nearest-neighbour Template Matching,
Representing Knowledge of Facial Appearance, Statistical Knowledge of
Facial Appearance, Statistical Knowledge of Identity, Structural
Knowledge: The Role of Correspondence, Biological Perspectives,
Discussion

9 Multi-View Identification 
View-based Models, The Role of Prior Knowledge, View Correspondence in
Identification, Generalisation from a Single View, Generalisation from
Multiple Views, Biological Perspectives, Discussion

10 Identifying Moving Faces 
Biological Perspectives, Computational Theories of Temporal
Identification, Identification using Holistic Temporal Trajectories,
Identification by Continuous View Transformation, An Experimental
System, Discussion

PART IV     PERCEPTION IN CONTEXT 

11 Perceptual Integration 
Sensory and Model-based Vision, Perceptual Fusion, Perceptual
Inference, Vision as Co-operating Processes, Biological Perspectives,
Discussion

12 Beyond Faces 
Multi-modal Identification, Visually Mediated Interaction, Visual
Surveillance and Monitoring, Immersive Virtual Reality, Visual
Database Screening

PART V    APPENDICES 

A Databases 
Database Acquisition and Design, Acquisition of a Pose-labelled
Database, Benchmarking, Commercial Databases, Public Domain Face
Databases, Discussion

B Commercial Systems 
System Characterisation, A View on the Industry, Discussion 

C Mathematical Details 
Principal Components Analysis, Linear Discriminant Analysis, Gaussian
Mixture Estimation, Kalman Filters, Bayesian Belief Networks, Hidden
Markov Models, Gabor Wavelets

Bibliography 

Index 

344 pp.