1. 1 Per-Pixel Opacity Modulation for Feature Enhancement in Volume Rendering Speaker: 吳昱慧 Date:2010/11/16 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

2. 2 Outline Introduction Reformulating Volume Rendering Using A Relevance Function Relevance Functions Method Adjustments Preintegration Implementation Results Conclusions

3. 3 Introduction Traditional direct volume rendering (DVR) techniques consist in integrating opacity and color values Direct volume rendering allows one to display multiple values contributing to one pixel

4. 4 In order to resolve the crucial issue of simultaneous visibility of data features in a view-independent fashion In order to identify empty areas, a binary classification of the data was required

5. 5 Reformulating Volume Rendering Using A Relevance Function Solving the visibility issues pertaining to volume rendering by dynamically adapting the opacity of the fragment contributions at a per- pixel level Based on the use of a relevance function quantifying

6. 6 Per-Pixel Adaptive DVR The classical direct volume rendering integral is then as follows It can be approximated by the following Riemann sum

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8. 8 In order to keep all structures visible that project to a given pixel, we use the same extinction coefficient for each term To maximize this function, we take its derivative and find its zeroes and, respectively, mean fully transparent and fully opaque

9. 9 Per-Pixel Adaptive Volume Rendering The additive blending volume rendering integral is as follows Approximated by a Riemann sum in the following way

10. 10 Introduce classification in the context of additive volume rendering In order to avoid saturating colors at a given pixel with our additive technique

11. 11 Relevance Functions Study the influence of the relevance function Consists of using a binary function Weighting the relevant contributions instead of considering them as all being equal f(x,y,z) is the function we want to visualize, the relevance of a contribution is defined as

12. 12 Tried to use a thresholding of gradient values, that is, the relevance function is defined as Using the second order derivative or Laplacian as a relevance criterion, that is

13. 13 Simple boundary detection as follows

14. 14 Method Adjustments This bound was determined experimentally to be within the [0.1,0.2] range

15. 15 In order to avoid giving false information about the data set, spatial continuity should be ensured in screen space Using Gaussian blur

16. 16 Show two times and four times scaling Improve the interactivity of our technique at the expense of visual quality

17. 17 Increased depth perception

18. 18 Improve the perception of shapes This consists of replacing the color with the following formula

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20. 20 Preintegration An important feature for volume rendering Starting from the additive adaptive volume rendering integral

21. 21 Improves the quality of the pictures

22. 22 Implementation

23. 23 Results In all tests, the binary function used is based on the opacity function of the DVR rendering, and is 1 when the opacity function is greater than zero, and 0 otherwise

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26. 26 Conclusions Introduced a new simple volume rendering technique Does not require any complex opacity function setup Using standard border detection techniques (gradient or border detection) as the relevance function leads to the best results