Selective visual attention : computational models and applications / / Liming Zhang, Weisi Lin |
Autore | Zhang Liming <1943-> |
Edizione | [1st edition] |
Pubbl/distr/stampa | Singapore : , : John Wiley & Sons Inc., , 2013 |
Descrizione fisica | 1 online resource (348 p.) |
Disciplina | 006.3/7 |
Altri autori (Persone) | LinWeisi |
Soggetto topico |
Computer vision
Selectivity (Psychology) - Computer simulation |
ISBN |
1-118-06005-9
1-299-31595-X 0-470-82813-7 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
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. |
Record Nr. | UNINA-9910139058603321 |
Zhang Liming <1943-> | ||
Singapore : , : John Wiley & Sons Inc., , 2013 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Selective visual attention : computational models and applications / / Liming Zhang, Weisi Lin |
Autore | Zhang Liming <1943-> |
Edizione | [1st edition] |
Pubbl/distr/stampa | Singapore : , : John Wiley & Sons Inc., , 2013 |
Descrizione fisica | 1 online resource (348 p.) |
Disciplina | 006.3/7 |
Altri autori (Persone) | LinWeisi |
Soggetto topico |
Computer vision
Selectivity (Psychology) - Computer simulation |
ISBN |
1-118-06005-9
1-299-31595-X 0-470-82813-7 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
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. |
Record Nr. | UNINA-9910808327903321 |
Zhang Liming <1943-> | ||
Singapore : , : John Wiley & Sons Inc., , 2013 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Visual Quality Assessment by Machine Learning [[electronic resource] /] / by Long Xu, Weisi Lin, C.-C. Jay Kuo |
Autore | Xu Long |
Edizione | [1st ed. 2015.] |
Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2015 |
Descrizione fisica | 1 online resource (142 p.) |
Disciplina | 006.31 |
Collana | SpringerBriefs in Signal Processing |
Soggetto topico |
Signal processing
Image processing Speech processing systems Optical data processing Computational intelligence Signal, Image and Speech Processing Image Processing and Computer Vision Computational Intelligence |
ISBN | 981-287-468-2 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction -- Fundamental knowledges of machine learning -- Image features and feature processing -- Feature pooling by learning -- Metrics fusion -- Summary and remarks for future research. |
Record Nr. | UNINA-9910299826503321 |
Xu Long | ||
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2015 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|
Visual Signal Quality Assessment [[electronic resource] ] : Quality of Experience (QoE) / / edited by Chenwei Deng, Lin Ma, Weisi Lin, King Ngi Ngan |
Edizione | [1st ed. 2015.] |
Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
Descrizione fisica | 1 online resource (311 p.) |
Disciplina |
006.6
620 621.3815 621.382 |
Soggetto topico |
Electronic circuits
Signal processing Image processing Speech processing systems Computer graphics Circuits and Systems Signal, Image and Speech Processing Computer Graphics |
ISBN | 3-319-10368-7 |
Formato | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto | Introduction – State of the play and challenges of visual quality assessment -- How passive image viewers became active multimedia users: New trends and recent advances in subjective assessment of Quality of Experience -- Recent Advances in Objective Image Quality Assessment -- Quality assessment of mobile videos -- High Dynamic Range Visual Quality of Experience Measurement: Challenges and Perspectives. |
Record Nr. | UNINA-9910299845003321 |
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
Materiale a stampa | ||
Lo trovi qui: Univ. Federico II | ||
|