Computational Modeling of Visual Attention (1)
-
Upload
rahul-agrawal -
Category
Documents
-
view
219 -
download
1
description
Transcript of Computational Modeling of Visual Attention (1)
![Page 1: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/1.jpg)
Computational Modeling of Visual Attention
“Attention is the cognitive process of selectively concentrating on one aspect of the environment while ignoring other things.”
Presented By :Rahul Agrawal(1265EC65R11)Soumyajit Gupta(12EC65R14)
Under Guidance of :Dr. Jayanta MukhopadhyayDr. Ritwik Kumar Layek
![Page 2: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/2.jpg)
2
What is Attention ?Attention is the set of mechanisms that optimize/control the search processes inherent in vision.1. Select
1. Spatial region of interest.2. Temporal window of interest3. World/Task/Object/Event model.4. Gaze/Viewpoint
2. Restrict1. Task relevant search space pruning.2. Location cues.3. Fixation points.4. Search depth control.
3. Suppress1. Spatial/Feature surround inhibition.2. Inhibition of return. Computational Modelling of Visual
Attention
![Page 3: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/3.jpg)
Computational Modelling of Visual Attention
3
Factors governing AttentionBottom-Up Cues.Top-Down Cues.
Which bar catches your attention first ? Where is
Launchpad Mcquack ?
Fig. 1 Fig. 2
![Page 4: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/4.jpg)
Computational Modelling of Visual Attention
4
Retinal Structure• 120 million rods (intensity)• 7 million cones (color)• Fovea: 2 degrees of visual field
Fig. 3
![Page 5: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/5.jpg)
5
Psychophysical Models of AttentionTreisman’s Feature integration
theory.
Computational Modelling of Visual Attention
Wolfe’s Guided search model.
Fig. 4Fig. 5
![Page 6: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/6.jpg)
6
General flow of computational models
Extraction of feature maps.
Computational Modelling of Visual Attention
1. Intensity2. Color3. Orientation4. Foveation5. Motion6. Shape/Size7. Location8. Foreground/Background
Activation map of features.Normalization of activation maps.
Fig. 6
![Page 7: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/7.jpg)
7
Image pyramids
W
116
116
14
38
1/161/4
3/8
1/161/4
Where, O is orientation map at scale n and orientation alpha.
![Page 8: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/8.jpg)
Computational Modelling of Visual Attention
8
Computational Models of AttentionNo.
Model Year Ap.
Resolution
1. Koch & Ullman [ ] 1985 I w/16 x h/16
2. NVT by itti et al. [] 1998 I w/16 x h/16
3. VOCUS by frintrop et al.[] 2005 B w/4 x h/44. Saliency Toolbox [] 2006 I w/16 x
h/165. GBVS by harel et al. [] 2006 I wxh6. Spectral Residual [] 2007 I 64x647. Judd et al. 2009 I Wxh8. Achanta9. Sir10. Context aware11. DIVOG
![Page 9: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/9.jpg)
9
Koch & Ullman
Computational Modelling of Visual Attention
![Page 10: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/10.jpg)
10
NVT by itti et al./Saliency Toolbox
Computational Modelling of Visual Attention
![Page 11: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/11.jpg)
11
Spectral Residual
Computational Modelling of Visual Attention
![Page 12: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/12.jpg)
12
Achanta
Computational Modelling of Visual Attention
![Page 13: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/13.jpg)
13
DIVOG
Computational Modelling of Visual Attention
![Page 14: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/14.jpg)
14
VOCUS : Bottom-Up part
Computational Modelling of Visual Attention
(Visual Object detection with Computational attention System)
• Three different feature dimensionsare computed independently.• Compute image pyramids ofcorresponding features. • Scale maps I’’,O’’,C’’ are computedusing center surround mechanism.• Scale maps are then fused to getdifferent feature maps(I’,O’,C’).S
TEP 1: All maps are resized to scale S2.
STEP 2: The maps are added up pixel by pixel.For eg Intensity feature map(I’)
![Page 15: Computational Modeling of Visual Attention (1)](https://reader035.fdocuments.in/reader035/viewer/2022062502/5695d1d51a28ab9b0298179b/html5/thumbnails/15.jpg)
Computational Modelling of Visual Attention
15