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Focus of Attention for Volumetric Data Inspection

Focus of Attention for Volumetric Data Inspection . Ivan Viola 1 , Miquel Feixas 2 , Mateu Sbert 2 , and Meister Eduard Gr öller 1. 1 Institute of Computer Graphics and Algorithms Vienna University of Technology. 2 Institute of Informatics and Applications University of Girona. Goal.

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Focus of Attention for Volumetric Data Inspection

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  1. 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

  2. Goal Input: known and classified volumetric data High level request: show me feature X Output: visually pleasing focusing at X I. Viola, M. Feixas, M. Sbert, and M. E. Gröller

  3. Focusing Considerations Focus discrimination Characteristic viewpoint Smart focusing approach I. Viola, M. Feixas, M. Sbert, and M. E. Gröller

  4. Visual Focus Discrimination • Levels of sparseness • Dense for focus to visually pop-out • Sparse for context visually suppressed • Cut-aways to unveil internal features vessels kidneys intestine I. Viola, M. Feixas, M. Sbert, and M. E. Gröller

  5. visibility estimation v 2 image-space weight v v object selection by user 1 3 o o 2 2 o o 1 1 o o 3 3 information-theoretic framework for optimal viewpoint estimation importance distribution ... p(v ) p(o |v ) 1 1 1 ... ... m p(o |v ) Σ j i I(v ,O) = p(o |v ) log i j i p(o ) j j o o o 1 2 3 ... ... p(v ) p(o |v ) n m n ... p(o ) p(o ) m 1 Estimation of Characteristic Viewpoints I. Viola, M. Feixas, M. Sbert, and M. E. Gröller

  6. Guided Navigation • Focusing at feature X • Discrimination of X from context • Change to a characteristic viewpoint of X • Refocusing from feature X to feature Y • De-emphasis of feature X • Emphasis of feature Y • Change to general characteristic viewpoint • Change to characteristic viewpoint of Y I. Viola, M. Feixas, M. Sbert, and M. E. Gröller

  7. v o c o 2 1 o o 2 1 o 3 v v 2 1 Refocusing I. Viola, M. Feixas, M. Sbert, and M. E. Gröller

  8. Refocusing I. Viola, M. Feixas, M. Sbert, and M. E. Gröller

  9. Conclusions • Often no need to have all degrees of freedom • Users need smart tools • One image is more than thousands words • Visual story says more than thousand images I. Viola, M. Feixas, M. Sbert, and M. E. Gröller

  10. Proof Any Questions? I. Viola, M. Feixas, M. Sbert, and M. E. Gröller

  11. Thank you for your attention! Any Questions?

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