Selective Visual Attention
Home > Medicine & Health Science textbooks > Pre-clinical medicine: basic sciences > Physiology > Regional physiology > Selective Visual Attention: Computational Models and Applications(IEEE Press)
Selective Visual Attention: Computational Models and Applications(IEEE Press)

Selective Visual Attention: Computational Models and Applications(IEEE Press)

|
     0     
5
4
3
2
1




Out of Stock


Notify me when this book is in stock
About the Book

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

Table of Contents:
Preface xi 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 Computational Models in the Spatial Domain 75 3.1 Baseline Saliency Model for Images 75 3.1.1 Image Feature Pyramids 76 3.1.2 Centre–Surround Differences 79 3.1.3 Across-scale and Across-feature Combination 80 3.2 Modelling for Videos 81 3.2.1 Extension of BS Model for Video 81 3.2.2 Motion Feature Detection 81 3.2.3 Integration for Various Features 83 3.3 Variations and More Details of BS Model 84 3.3.1 Review of the Models with Variations 85 3.3.2 WTA and IoR Processing 87 3.3.3 Further Discussion 90 3.4 Graph-based Visual Saliency 91 3.4.1 Computation of the Activation Map 92 3.4.2 Normalization of the Activation Map 94 3.5 Attention Modelling Based on Information Maximizing 95 3.5.1 The Core of the AIM Model 96 3.5.2 Computation and Illustration of Model 97 3.6 Discriminant Saliency Based on Centre–Surround 101 3.6.1 Discriminant Criterion Defined on Centre–Surround 102 3.6.2 Mutual Information Estimation 103 3.6.3 Algorithm and Block Diagram of Bottom-up DISC Model 106 3.7 Saliency Using More Comprehensive Statistics 107 3.7.1 The Saliency in Bayesian Framework 108 3.7.2 Algorithm of SUN Model 110 3.8 Saliency Based on Bayesian Surprise 113 3.8.1 Bayesian Surprise 113 3.8.2 Saliency Computation Based on Surprise Theory 114 3.9 Summary 116 References 117 4 Fast Bottom-up Computational Models in the Spectral Domain 119 4.1 Frequency Spectrum of Images 120 4.1.1 Fourier Transform of Images 120 4.1.2 Properties of Amplitude Spectrum 121 4.1.3 Properties of the Phase Spectrum 123 4.2 Spectral Residual Approach 123 4.2.1 Idea of the Spectral Residual Model 124 4.2.2 Realization of Spectral Residual Model 125 4.2.3 Performance of SR Approach 126 4.3 Phase Fourier Transform Approach 127 4.3.1 Introduction to the Phase Fourier Transform 127 4.3.2 Phase Fourier Transform Approach 128 4.3.3 Results and Discussion 129 4.4 Phase Spectrum of the Quaternion Fourier Transform Approach 131 4.4.1 Biological Plausibility for Multichannel Representation 131 4.4.2 Quaternion and Its Properties 132 4.4.3 Phase Spectrum of Quaternion Fourier Transform (PQFT) 134 4.4.4 Results Comparison 138 4.4.5 Dynamic Saliency Detection of PQFT 140 4.5 Pulsed Discrete Cosine Transform Approach 141 4.5.1 Approach of Pulsed Principal Components Analysis 141 4.5.2 Approach of the Pulsed Discrete Cosine Transform 143 4.5.3 Multichannel PCT Model 144 4.6 Divisive Normalization Model in the Frequency Domain 145 4.6.1 Equivalent Processes with a Spatial Model in the Frequency Domain 146 4.6.2 FDN Algorithm 149 4.6.3 Patch FDN 150 4.7 Amplitude Spectrum of Quaternion Fourier Transform (AQFT) Approach 152 4.7.1 Saliency Value for Each Image Patch 152 4.7.2 The Amplitude Spectrum for Each Image Patch 153 4.7.3 Differences between Image Patches and their Weighting to Saliency Value 154 4.7.4 Patch Size and Scale for Final Saliency Value 156 4.8 Modelling from a Bit-stream 157 4.8.1 Feature Extraction from a JPEG Bit-stream 157 4.8.2 Saliency Detection in the Compressed Domain 160 4.9 Further Discussions of Frequency Domain Approach 161 References 163 5 Computational Models for Top-down Visual Attention 167 5.1 Attention of Population-based Inference 168 5.1.1 Features in Population Codes 170 5.1.2 Initial Conspicuity Values 171 5.1.3 Updating and Transformation of Conspicuity Values 173 5.2 Hierarchical Object Search with Top-down Instructions 175 5.2.1 Perceptual Grouping 175 5.2.2 Grouping-based Salience from Bottom-up Information 176 5.2.3 Top-down Instructions and Integrated Competition 179 5.2.4 Hierarchical Selection from Top-down Instruction 179 5.3 Computational Model under Top-down Influence 180 5.3.1 Bottom-up Low-level Feature Computation 181 5.3.2 Representation of Prior Knowledge 181 5.3.3 Saliency Map Computation using Object Representation 184 5.3.4 Using Attention for Object Recognition 184 5.3.5 Implementation 185 5.3.6 Optimizing the Selection of Top-down Bias 186 5.4 Attention with Memory of Learning and Amnesic Function 187 5.4.1 Visual Memory: Amnesic IHDR Tree 188 5.4.2 Competition Neural Network Under the Guidance of Amnesic IHDR 191 5.5 Top-down Computation in the Visual Attention System: VOCUS 193 5.5.1 Bottom-up Features and Bottom-up Saliency Map 193 5.5.2 Top-down Weights and Top-down Saliency Map 194 5.5.3 Global Saliency Map 196 5.6 Hybrid Model of Bottom-up Saliency with Top-down Attention Process 196 5.6.1 Computation of the Bottom-up Saliency Map 197 5.6.2 Learning of Fuzzy ART Networks and Top-down Decision 197 5.7 Top-down Modelling in the Bayesian Framework 199 5.7.1 Review of Basic Framework 200 5.7.2 The Estimation of Conditional Probability Density 201 5.8 Summary 202 References 202 6 Validation and Evaluation for Visual Attention Models 207 6.1 Simple Man-made Visual Patterns 207 6.2 Human-labelled Images 208 6.3 Eye-tracking Data 209 6.4 Quantitative Evaluation 211 6.4.1 Some Basic Measures 211 6.4.2 ROC Curve and AUC Score 213 6.4.3 Inter-subject ROC Area 213 6.5 Quantifying the Performance of a Saliency Model to Human Eye Movement in Static and Dynamic Scenes 215 6.6 Spearman’s Rank Order Correlation with Visual Conspicuity 217 References 219 PART III APPLICATIONS OF ATTENTION SELECTION MODELS 221 7 Applications in Computer Vision, Image Retrieval and Robotics 223 7.1 Object Detection and Recognition in Computer Vision 224 7.1.1 Basic Concepts 224 7.1.2 Feature Extraction 224 7.1.3 Object Detection and Classification 227 7.2 Attention Based Object Detection and Recognition in a Natural Scene 231 7.2.1 Object Detection Combined with Bottom-up Model 231 7.2.2 Object Detection based on Attention Elicitation 233 7.2.3 Object Detection with a Training Set 236 7.2.4 Object Recognition Combined with Bottom-up Attention 239 7.3 Object Detection and Recognition in Satellite Imagery 240 7.3.1 Ship Detection based on Visual Attention 242 7.3.2 Airport Detection in a Land Region 245 7.3.3 Saliency and Gist Feature for Target Detection 248 7.4 Image Retrieval via Visual Attention 250 7.4.1 Elements of General Image Retrieval 251 7.4.2 Attention Based Image Retrieval 253 7.5 Applications of Visual Attention in Robots 256 7.5.1 Robot Self-localization 257 7.5.2 Visual SLAM System with Attention 259 7.5.3 Moving Object Detection using Visual Attention 262 7.6 Summary 265 References 265 8 Application of Attention Models in Image Processing 271 8.1 Attention-modulated Just Noticeable Difference 271 8.1.1 JND Modelling 272 8.1.2 Modulation via Non-linear Mapping 274 8.1.3 Modulation via Foveation 276 8.2 Use of Visual Attention in Quality Assessment 277 8.2.1 Image/Video Quality Assessment 278 8.2.2 Weighted Quality Assessment by Salient Values 279 8.2.3 Weighting through Attention-modulated JND Map 280 8.2.4 Weighting through Fixation 281 8.2.5 Weighting through Quality Distribution 281 8.3 Applications in Image/Video Coding 282 8.3.1 Image and Video Coding 282 8.3.2 Attention-modulated JND based Coding 284 8.3.3 Visual Attention Map based Coding 285 8.4 Visual Attention for Image Retargeting 287 8.4.1 Literature Review for Image Retargeting 288 8.4.2 Saliency-based Image Retargeting in the Compressed Domain 289 8.5 Application in Compressive Sampling 292 8.5.1 Compressive Sampling 293 8.5.2 Compressive Sampling via Visual Attention 296 8.6 Summary 300 References 300 PART IV SUMMARY 305 9 Summary, Further Discussions and Conclusions 307 9.1 Summary 308 9.1.1 Research Results from Physiology and Anatomy 308 9.1.2 Research from Psychology and Neuroscience 309 9.1.3 Theory of Statistical Signal Processing 310 9.1.4 Computational Visual Attention Modelling 310 9.1.5 Applications of Visual Attention Models 313 9.2 Further Discussions 314 9.2.1 Interaction between Top-down Control and Bottom-up Processing in Visual Search 314 9.2.2 How to Deploy Visual Attention in the Brain? 315 9.2.3 Role of Memory in Visual Attention 316 9.2.4 Mechanism of Visual Attention in the Brain 316 9.2.5 Covert Visual Attention 317 9.2.6 Saliency of Large Smooth Objects 317 9.2.7 Invariable Feature Extraction 320 9.2.8 Role of Visual Attention Models in Applications 320 9.3 Conclusions 320 References 321 Index 325


Best Sellers


Product Details
  • ISBN-13: 9780470828120
  • Publisher: John Wiley & Sons Inc
  • Publisher Imprint: Wiley-IEEE Press
  • Height: 241 mm
  • No of Pages: 352
  • Returnable: N
  • Spine Width: 23 mm
  • Weight: 658 gr
  • ISBN-10: 0470828129
  • Publisher Date: 17 May 2013
  • Binding: Hardback
  • Language: English
  • Returnable: N
  • Series Title: IEEE Press
  • Sub Title: Computational Models and Applications
  • Width: 163 mm


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
Selective Visual Attention: Computational Models and Applications(IEEE Press)
John Wiley & Sons Inc -
Selective Visual Attention: Computational Models and Applications(IEEE Press)
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Selective Visual Attention: Computational Models and Applications(IEEE Press)

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    New Arrivals

    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!