Nara Women’s University 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 Nara Women’s University Background Importance to select good viewpoints Problem of viewpoint selection Complex object Large-scale data a lot of visualized information huge calculation cost of rendering no criteria for good view ｙ-axis x-axis ？ z-axis ？ data of enough knowledge difficult to Need select good viewpoints time data & visualization technique etc. Nara Women’s University 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 Nara Women’s University Definition of Good Views No common definition Local definitions depending on each purpose Necessary information → visibility Unnecessary information → invisibility Good View information NEED USELESS Nara Women’s University 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 Nara Women’s University Viewpoint Entropy Nf Ai Ai I ( S , p ) log At i 0 At 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 Nara Women’s University Re-experiment of Viewpoint Entropy (1/2) RE-1 projected area is moved. The number of visible faces is constant best view Movement of a camera As the projected area increases, Viewpoint Entropy increases Nara Women’s University 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 best view As the number of visible faces increases, Viewpoint Entropy increases Nara Women’s University A Problem of Viewpoint Entropy The same Viewpoint Entropy value Difference in information of views Nara Women’s University 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 Nara Women’s University Proposal of View Potential n W ( Ai , 0 *Wi ,0 Ai ,1 *Wi ,1 Ai , 2 *Wi , 2 ) * Ai ,3 *Wi ,3 i 0 W0： projected area & the number of visible faces W1：luminance W2：chrominance W3：weight of objects Nara Women’s University W1: Luminance(1/2) Dark picture Brightness is 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 Nara Women’s University recognition. W1: Luminance(2/2) Calculation of viewpoint selection with view luminance YIQ Color System 【Y(Luminance ）,I & Q(Chrominance ）】 convert RGB into YIQ 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 Nara Women’s University Luminance Property What’s a good view in luminance ? The value of luminance diffuses. Larger dispersion in luminance should be selected. Nara Women’s University W2: chrominance cognition is difference in hue red-green yellow-blue chrominance in data Easy Difficult RGB Color System chrominance in perception different impressions by color mapping bury the difference of color recognition! L*a*b*Color System Nara Women’s University Chrominance Property Views with higher space frequency are more recognizable. The use of a differentiation filter edge Nara Women’s University 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 Nara Women’s University Visualization Pipeline (1/2) BYU Data Create Scene vtkBYUReader vtkCubeSource vtkPolyDataNomals * Generate a Scene * vtkPolyDataMapper The polygon object is set up vtkActor vtkActor in vtkRenderWindow vtkPolyDataMapper 3DS Data vtk3DSImporter vtkRender vtkRenderWindow Nara Women’s University Pipeline of visualization(2/2) vtkRenderWindow ActorList vtkActorCollection vtkActor vtkTriangleFilter vtkMassEntropy Implemented library GetInformation Calculate Entropy take out an Actor of the scene. calculate NULL each To use object. vtkMassEntropy the cell of the polygon is calculate normalized. information Nara Women’s University vtkMassEntropy Functions vtkMassEntropy SetInput(vtkPolyData); Input data necessary for calculation SetActors(vtk ActorCollection) SetWeight(int) Input the weight of each object GetEntropy() Calculate the Viewpoint Entropy GetChromi() Calculate chrominance GetCont(vktRenderer) Calculate contrast Nara Women’s University RE-3 Viewpoint Entropy+ Luminance Add the property of brightness to RE-1 Select asymmetry and a contrasty view entropy entrpy＋luminance Nara Women’s University ECMWF Experiment of Chrominance ：Data description RE-4 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 Nara Women’s University Comparison of images from experiment results (1/2) High appraisal Large deviation Low appraisal Small deviation Nara Women’s University Comparison of images from experiment result (2/2) High appraisal Low appraisal Almost same by human vision Nara Women’s University Change hue Complex temperature change High appraisal Simple temperature change Low appraisal The impression changes by hue Nara Women’s University Weighting Objects: Environment RE-5 A scene with several objects A camera moves with a constant distance around the focus point. Nara Women’s University Weighting Objects no weighting weighting set a value to this object Nara Women’s University ViewSet Change the coefficients of each property A set of good viewpoints Nara Women’s University 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. Nara Women’s University 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 Nara Women’s University 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 Nara Women’s University
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