computational modeling of visual attention (1)

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

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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

Computational Modelling of Visual Attention

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Factors governing AttentionBottom-Up Cues.Top-Down Cues.

Which bar catches your attention first ? Where is

Launchpad Mcquack ?

Fig. 1 Fig. 2


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Retinal Structure• 120 million rods (intensity)• 7 million cones (color)• Fovea: 2 degrees of visual field

Fig. 3

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Psychophysical Models of AttentionTreisman’s Feature integration

theory.


Wolfe’s Guided search model.

Fig. 4Fig. 5

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General flow of computational models

Extraction of feature maps.


1. Intensity2. Color3. Orientation4. Foveation5. Motion6. Shape/Size7. Location8. Foreground/Background

Activation map of features.Normalization of activation maps.

Fig. 6

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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.


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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

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Koch & Ullman


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NVT by itti et al./Saliency Toolbox


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Spectral Residual


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Achanta


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DIVOG


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VOCUS : Bottom-Up part


(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’)


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computational modeling of visual attention (1)

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