Selective visual attention: computational models and applications
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Singapore
Wiley
2013
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XIII, 332 S. Ill., |
| ISBN: | 9780470828120 |
Staff View
MARC
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| 020 | |a 9780470828120 |c hbk |9 978-0-470-82812-0 | ||
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| 040 | |a DE-604 |b ger |e aacr | ||
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| 100 | 1 | |a Zhang, Liming |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Selective visual attention |b computational models and applications |c Liming Zhang ; Weisi Lin |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Singapore |b Wiley |c 2013 | |
| 300 | |a XIII, 332 S. |b Ill., | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 0 | 7 | |a Maschinelles Sehen |0 (DE-588)4129594-8 |2 gnd |9 rswk-swf |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-025972392 | ||
Record in the Search Index
| _version_ | 1804150318894678016 |
|---|---|
| adam_text | Contents
Preface
PART I BASIC CONCEPTS AND THEORY
1
1
Introduction to Visual Attention
3
1.1
The Concept of Visual Attention
3
/././
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 (GSl)
42
vi
Contents
2.3.3
Revised Guided Sear eh 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 Synehrony
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 loR 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
Contents
Fast
Bottom-up Computational
Models in
the Spectral
Domain 119
4.1
Frequency Spectrum of Images
120
4.Ì.1
Fourier Transform of Images
120
4.1.2
Proper I ies 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 arid 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 Saliemy 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
Computational Models for Top-down Visual Attention
167
5.1
Attention of Population-based Inference
168
5././
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
viji
Contents
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
Compétition
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
Curre
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
Contents
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 Cist 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. /
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
Contents
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 Uirge 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
|
| any_adam_object | 1 |
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| id | DE-604.BV040994666 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:37:02Z |
| institution | BVB |
| isbn | 9780470828120 |
| language | English |
| lccn | 2012042377 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025972392 |
| oclc_num | 854709481 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG |
| owner_facet | DE-473 DE-BY-UBG |
| physical | XIII, 332 S. Ill., |
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| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Wiley |
| record_format | marc |
| spelling | Zhang, Liming Verfasser aut Selective visual attention computational models and applications Liming Zhang ; Weisi Lin 1. publ. Singapore Wiley 2013 XIII, 332 S. Ill., txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Maschinelles Sehen (DE-588)4129594-8 gnd rswk-swf Visuelle Aufmerksamkeit (DE-588)4329020-6 gnd rswk-swf Visuelle Aufmerksamkeit (DE-588)4329020-6 s Maschinelles Sehen (DE-588)4129594-8 s DE-604 Lin, Weisi Sonstige (DE-588)101175536X oth Erscheint auch als Online-Ausgabe 978-0-470-82813-7 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025972392&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zhang, Liming Selective visual attention computational models and applications Maschinelles Sehen (DE-588)4129594-8 gnd Visuelle Aufmerksamkeit (DE-588)4329020-6 gnd |
| subject_GND | (DE-588)4129594-8 (DE-588)4329020-6 |
| title | Selective visual attention computational models and applications |
| title_auth | Selective visual attention computational models and applications |
| title_exact_search | Selective visual attention computational models and applications |
| title_full | Selective visual attention computational models and applications Liming Zhang ; Weisi Lin |
| title_fullStr | Selective visual attention computational models and applications Liming Zhang ; Weisi Lin |
| title_full_unstemmed | Selective visual attention computational models and applications Liming Zhang ; Weisi Lin |
| title_short | Selective visual attention |
| title_sort | selective visual attention computational models and applications |
| title_sub | computational models and applications |
| topic | Maschinelles Sehen (DE-588)4129594-8 gnd Visuelle Aufmerksamkeit (DE-588)4329020-6 gnd |
| topic_facet | Maschinelles Sehen Visuelle Aufmerksamkeit |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025972392&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT zhangliming selectivevisualattentioncomputationalmodelsandapplications AT linweisi selectivevisualattentioncomputationalmodelsandapplications |