Focus of Attention for Volumetric Data Inspection

Ivan Viola1, Miquel Feixas2,Mateu Sbert2, and Meister Eduard Gröller1

1 Institute of Computer Graphics and Algorithms

Vienna University of Technology

2 Institute of Informatics and Applications

University of Girona

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Goal

Input: known and classified volumetric dataHigh level request: show me feature XOutput: visually pleasing focusing at X

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Focusing Considerations

Focus discrimination Characteristic viewpointSmart focusing approach

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Visual Focus Discrimination

Levels of sparsenessDense for focus to visually pop-outSparse for context visually suppressed

Cut-aways to unveil internal features

vessels

intestinekidneys

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Estimation of Characteristic Viewpoints

o2 o3o1

importance distribution

o1

o2

o3

object selection by user v1

v2

v3

o1

o2

o3

visibility estimationimage-space weight

p(v1)

p(vn)

p(o1|v1)

p(om|vn)

p(o1) p(om)

...

I(vi,O) = p(oj|vi) logΣj

m p(oj|vi)p(oj)

...

...

information-theoretic frameworkfor optimal viewpoint estimation

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Guided Navigation

Focusing at feature XDiscrimination of X from contextChange to a characteristic viewpoint of X

Refocusing from feature X to feature YDe-emphasis of feature XEmphasis of feature YChange to general characteristic viewpointChange to characteristic viewpoint of Y

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Refocusing

o1 o2

o3

vc

v1 v2

o1 o2

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Refocusing

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Conclusions

Often no need to have all degrees of freedomUsers need smart tools

One image is more than thousands wordsVisual story says more than thousand images

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Proof

Any Questions?

Thank you for your attention!Any Questions?