Visual attention is a relatively new area of study combining a
number of disciplines: artificial neural networks, artificial
intelligence, vision science and psychology. The aim is to
build computational models similar to human vision in order to
solve tough problems for many potential applications including
object recognition, unmanned vehicle navigation, and image and
video coding and processing. In this book, the authors provide an
up to date and highly applied introduction to the topic of visual
attention, aiding researchers in creating powerful computer vision
systems. Areas covered include the significance of vision research,
psychology and computer vision, existing computational visual
attention models, and the authors' contributions on visual
attention models, and applications in various image and video
processing tasks.

This book is geared for graduates students and researchers in
neural networks, image processing, machine learning, computer
vision, and other areas of biologically inspired model building and
applications. The book can also be used by practicing engineers
looking for techniques involving the application of image coding,
video processing, machine vision and brain-like robots to
real-world systems. Other students and researchers with
interdisciplinary interests will also find this book appealing.

* Provides a key knowledge boost to developers of image
processing applications

* Is unique in emphasizing the practical utility of attention
mechanisms

* Includes a number of real-world examples that readers can
implement in their own work:

* robot navigation and object selection

* image and video quality assessment

* image and video coding

* Provides codes for users to apply in practical attentional
models and mechanisms

Liming Zhang is a Professor of Electronics at Fudan
University, where she leads the Image and Intelligence Laboratory.
Since the 1980s she has been engaged in biological modeling and its
application to engineering, such as artificial neural network
models, visual models and brain-like robot models, and has
published three books in Chinese on artificial neural networks,
image coding and intelligent image processing, as well as over 120
pages in the area. Since 2003 she has been studying problems
in modeling visual attention and applying it in computer vision,
robot vision, object tracking, remote sensing and image quality
assessment. She has served as a Senior Visiting Scholar at the
University of Notre Dame and Technical University of Munich.

Weisi Lin is an Associate Professor in the division of
computer communications at Nanyang Technological University's
School of Computer Engineering. He also serves as Lab Head, Visual
Processing, and Acting Department Manager, Media Processing, in
Institute for Infocomm Research. Lin has also participated in
research at Shantou University (China), Bath University (UK),
National University of Singapore, Institute of Microelectronics
(Singapore), Centre for Signal Processing (Singapore). His research
interests include image processing, perceptual modeling, video
compression, multimedia communication and computer vision. He holds
10 patents, has written 4 book chapters, and has published over 130
refereed papers in international journals and conferences. He is a
Chartered Engineer, and a Fellow of IET. Lin graduated from
Zhongshan University, China with B.Sc in Electronics and M.Sc in
Digital Signal Processing, and from King's College, London
University, UK with Ph.D in Computer Vision.

This book is geared for graduates students and researchers in neural networks, image processing, machine learning, computer vision, and other areas of biologically inspired model building and applications. The book can also be used by practicing engineers looking for techniques involving the application of image coding, video processing, machine vision and brain-like robots to real-world systems. Other students and researchers with interdisciplinary interests will also find this book appealing.

• Provides a key knowledge boost to developers of image processing applications
• Is unique in emphasizing the practical utility of attention mechanisms
• Includes a number of real-world examples that readers can implement in their own work:
• robot navigation and object selection
• image and video quality assessment
• image and video coding
• Provides codes for users to apply in practical attentional models and mechanisms

PART I BASIC CONCEPTS AND THEORY 1

1 Introduction to Visual Attention 3

1.1 The Concept of Visual Attention 3

1.1.1 Selective Visual Attention 3

1.1.2 What Areas in a Scene Can Attract Human Attention? 4

1.1.3 Selective Attention in Visual Processing 5

1.2 Types of Selective Visual Attention 7

1.2.1 Pre-attention and Attention 7

1.2.2 Bottom-up Attention and Top-down Attention 8

1.2.3 Parallel and Serial Processing 10

1.2.4 Overt and Covert Attention 11

1.3 Change Blindness and Inhibition of Return 11

1.3.1 Change Blindness 11

1.3.2 Inhibition of Return 12

1.4 Visual Attention Model Development 12

1.4.1 First Phase: Biological Studies 13

1.4.2 Second Phase: Computational Models 15

1.4.3 Third Phase: Visual Attention Applications 17

1.5 Scope of This Book 18

References 19

2 Background of Visual Attention Theory and Experiments 25

2.1 Human Visual System (HVS) 25

2.1.1 Information Separation 26

2.1.2 Eye Movement and Involved Brain Regions 28

2.1.3 Visual Attention Processing in the Brain 29

2.2 Feature Integration Theory (FIT) of Visual Attention 29

2.2.1 Feature Integration Hypothesis 30

2.2.2 Confirmation by Visual Search Experiments 31

2.3 Guided Search Theory 39

2.3.1 Experiments: Parallel Process Guides Serial Search 40

2.3.2 Guided Search Model (GS1) 42

2.3.3 Revised Guided Search Model (GS2) 43

2.3.4 Other Modified Versions: (GS3, GS4) 46

2.4 Binding Theory Based on Oscillatory Synchrony 47

2.4.1 Models Based on Oscillatory Synchrony 49

2.4.2 Visual Attention of Neuronal Oscillatory Model 54

2.5 Competition, Normalization and Whitening 56

2.5.1 Competition and Visual Attention 56

2.5.2 Normalization in Primary Visual Cortex 57

2.5.3 Whitening in Retina Processing 59

2.6 Statistical Signal Processing 60

2.6.1 A Signal Detection Approach for Visual Attention 61

2.6.2 Estimation Theory and Visual Attention 62

2.6.3 Information Theory for Visual Attention 63

References 67

PART II COMPUTATIONAL ATTENTION MODELS 73

3 Com…