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Automatic Viewpoint Selection for a Visualization I/F in a PSE

Automatic Viewpoint Selection for a Visualization I/F in a PSE. Machiko Nakagawa, Masami Takata, Kazuki Joe Nara Women’s University. Outline. Background Explain the Viewpoint Entropy Proposal of View Potential Experiment Discussions Conclusions & Future work. x-axis. ｙ -axis. ？.

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Automatic Viewpoint Selection for a Visualization I/F in a PSE

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  1. Automatic Viewpoint Selection for a Visualization I/F in a PSE Machiko Nakagawa, Masami Takata, Kazuki Joe Nara Women’s University

  2. Outline • Background • Explain the Viewpoint Entropy • Proposal of View Potential • Experiment • Discussions • Conclusions & Future work

  3. x-axis ｙ-axis ？ z-axis ？ data time etc. Background • Importance to select good viewpoints • Problem of viewpoint selection • a lot of visualized information • huge calculation cost of rendering • no criteria for good view Complex object Large-scale data Need enough knowledge of data & visualization technique difficult to select good viewpoints

  4. View Selection in PSE • Possible visualization without expertise in PSE. • View selection by user • Eager of automatic viewpoint selection • possibility of easier visualization Technique of Automatic Viewpoint Selection with versatility

  5. Good View information NEED USELESS Definition of Good Views • No common definition • Local definitions depending on each purpose • Necessary information　→　visibility • Unnecessary information　→　invisibility

  6. Previous Works • Vázquez, “Viewpoint Selection Using Viewpoint Entropy“(2001) • A viewpoint definition by information theory • Shannon’s Entropy • Viewpoint Entropy • projected Area • the number of visible faces

  7. Viewpoint Entropy Nf：the number of faces of the scene Ai：projected area of a face i A0：projected area of the background in open scenes At：the total projected area of the scene

  8. RE-1 Re-experiment of Viewpoint Entropy (1/2) • projected area is moved. • The number of visible faces is constant best view As the projected area increases, Viewpoint Entropy increases Movement of a camera

  9. best view RE-2 Re-experiment of Viewpoint Entropy(2/2) • The number of visible faces is increased. • The projected area is almost same as the previous experiment As the number of visible faces increases, Viewpoint Entropy increases

  10. A Problem of Viewpoint Entropy The same Viewpoint Entropy value Difference in information of views

  11. Improvement of Viewpoint Entropy • Only two properties for viewpoint selection • No other properties which should be • Brightness, Color,etc. problems of Viewpoint Entropy plural properties to obtain better views Improvement of evaluation method View Potential

  12. Proposal of View Potential • W0： projected area& the number of visible faces • W1：luminance • W2：chrominance • W3：weight of objects

  13. W1: Luminance(1/2) Dark picture • Brightnessis more sensitive than color difference for human perception EX) Dark place and/or very small object Bright picture Recognize shape(brightness) • Unrecognize • color difference Luminance is important for scene recognition.

  14. Y = 0.2990 * R + 0.5870 * G + 0.1140 * B I = 0.5959 * R - 0.2750 * G + 0.3210 * B Q = 0.2065 * R - 0.4969 * G - 0.2904 * B W1: Luminance(2/2) • Calculation of viewpoint selection with view luminance • YIQ Color System • 【Y(Luminance）,I & Q(Chrominance）】 convert RGB into YIQ

  15. Luminance Property • What’s a good view in luminance ? • The value of luminance diffuses. • Larger dispersion in luminance should be selected.

  16. W2: chrominance • cognition is difference in hue • red-green • yellow-blue chrominance in data Easy RGB Color System Difficult chrominance in perception different impressions by color mapping bury the difference of color recognition! L*a*b*Color System

  17. Chrominance Property • Views with higher space frequency are more recognizable. • The use of a differentiation filter edge

  18. W3:Weight objects • Weight each object as the importance degree • The weight of unnecessary objects is 0 • Reduction of calculation cost weight:2 Need weight:1 No Need weight:0

  19. Visualization Pipeline (1/2) BYU Data Create Scene vtkBYUReader vtkCubeSource vtkPolyDataNomals * Generate a Scene * The polygon object is set up in vtkRenderWindow vtkPolyDataMapper vtkPolyDataMapper 3DS Data vtkActor vtkActor vtk3DSImporter vtkRender vtkRenderWindow

  20. Implemented library Pipeline of visualization(2/2) vtkRenderWindow take out an Actor of the scene. calculate each object. ActorList vtkActorCollection NULL To use vtkMassEntropy the cell of the polygon is normalized. vtkActor vtkTriangleFilter calculate information vtkMassEntropy GetInformation Calculate Entropy

  21. Calculate the Viewpoint Entropy Input the weight of each object Input data necessary for calculation Calculate contrast Calculate chrominance vtkMassEntropy • Functions vtkMassEntropy SetInput(vtkPolyData); SetActors(vtk ActorCollection) SetWeight(int) GetEntropy() GetChromi() GetCont(vktRenderer)

  22. RE-3 Viewpoint Entropy+ Luminance • Add the property of brightness to RE-1 Select asymmetry and a contrasty view entropy entrpy＋luminance

  23. RE-4 Experiment of Chrominance：Data description • ECMWF • The European Center for Medium-range Weather Forecasts • provide temperature data of the atmosphere.（1991/1/1) ・Height：Latitude ・Width：Longitude ・time：altitude ・color：temperature

  24. Comparison of images from experiment results (1/2) High appraisal Low appraisal Large deviation Small deviation

  25. Comparison of images from experiment result (2/2) High appraisal Low appraisal Almost same by human vision

  26. Change hue Complex temperature change Simple temperature change High appraisal Low appraisal The impression changes by hue

  27. RE-5 Weighting Objects:Environment • A scene with several objects A camera moves with a constant distance around the focus point.

  28. set a value to this object Weighting Objects weighting no weighting

  29. ViewSet • Change the coefficients of each property • A set of good viewpoints

  30. Discussions • luminance • calculating the contrast of the whole scene, • The detail of an object might not be presented. • improvement by the information of color difference • chrominance • Not only the chrominance values but also the chrominance degree based on human perception • application of texture mapping etc.

  31. Conclusions • An automatic and general viewpoint selection technique is proposed. • View Potential with plural properties is defined. • Experiments with some scenes, and selection of good views

  32. Future works • reduction of calculate cost • CPU GPU • use of general purpose shade pipes • calculate vtk library →directX or OpenGL • decrease the number of calculating points. • How to move camera • Appropriate coefficient for each property by GUI

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