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Scientific Visualization

Scientific Visualization. How Do Computers Make Images. Pictures are made up of pixels A pixel is a small box made up of all one color The smaller the pixels, the more easily the picture can be seen The more pixels you have the more memory the picture will take up.

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Scientific Visualization

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  1. Scientific Visualization

  2. How Do Computers Make Images • Pictures are made up of pixels • A pixel is a small box made up of all one color • The smaller the pixels, the more easily the picture can be seen • The more pixels you have the more memory the picture will take up

  3. How Do Computers Turn Raw Data Into Pictures? • Every piece of data in the array will become one pixel in the image • Every number in the array needs to be assigned a certain color

  4. Choosing The Colors For The Data The computer finds the maximum and minimum values in the array (4 and 0) The computer then splits the data in equal intervals between the max and min After the intervals are created each one is assigned a certain color. 0 (DARK BLUE) 1 (LIGHT BLUE) 2 (YELLOW) 3 (ORANGE) 4 (RED)

  5. The Final Picture The Original Array The Color Pallete

  6. Using Color • The range of colors your computer uses to create a picture is called a palette • This palette called a rainbow palette will assign data values near the maximum a red color and data values near the minimum a blue color

  7. Using The Rainbow Palette • This picture represents the electron density values for the electrons in a helium atom • A smooth set of color changes follows from blue to red associated with the max and min values in the data

  8. Using Different Types of Palettes • Banded palettes like the one at left do not employ smooth changes, they have very abrupt ones • This may make small differences in the data set more noticeable

  9. Rainbow vs. Banded Palette These are two different ways of viewing the same data

  10. Changing The Palette • Can make small differences in your data easier for the viewer to see • Can draw the viewer to certain parts of your image • Can cause the viewer to have misleading ideas of what your image represents

  11. Manipulating Your Images • Manipulation of your image is alright as long as you do not fundamentally change the image Interpolation is an example of this. Sometimes it is difficult to get enough data points to make a smooth image so we mathematically smooth one image into the next

  12. Interpolated Image Raw Image Advertise what data manipulation you have done, and be careful not to introduce artifacts into your data

  13. Recognize This Picture? This picture was taken in 1976 by cameras on a probe orbiting mars Each pixel on the screen represents an area of 2304 square yards

  14. After Mathematical Manipulation Of The Data

  15. Placed Side by Side

  16. An Example From Start To Finish • I have chosen to use data from the April 1995 Mayoral election in Chicago • I decided to visualize the percentage of the vote Mayor Daley received in the election

  17. Entering The Data • A map of the city’s voting precincts was found • A grid was imposed over this map • Every square in the grid became one piece of data

  18. The Dataset This is a part of the array which shows the percentage of the vote Daley received in different parts of the city.

  19. Imaging The Dataset • The data values are assigned colors • Deepest red is 100% of the vote • Deepest Blue is 0% of the vote

  20. Smoothing The Image

  21. Using Programs like Scion Image, it is possible to view the raw data along with the image

  22. Planetary Terrain Model This project was a huge undertaking of both the time required to plan it and the class time necessary to execute it. To justify the use of our most precious resource (time) we set several goals which we hoped to reach. • Practice gathering and recording data (Over 76,000 points) • Exposure to numerical analysis (both manually and with the computer) • Learning how to use computer imaging and visualization software • Exposure to computational computing including filter application and binary/logic operators • Learning state of the art modeling techniques • Learning how to construct a computer generated digital elevation model

  23. Phase 1: Creating The Terrain • In phase 1 of this project each class created a terrain on a sheet of plywood. Once construction and measurements were complete, the 4 sheets of plywood were put together

  24. Building The Terrain

  25. Phase 2: Taking Measurements Measurements were taken by inserting a metal rod through a grid system built into a table. The height from the table to the model was measured and entered into a spreadsheet.

  26. The Data Array These numbers represent the heights of a part of the model

  27. Phase 3: Analyzing the Data In the final stage, students will be taught how to use imaging software to convert their actual data into a viewable images. These images will then be analyzed. We will calibrate the image spatially, and then take measurements on both the physical model and the computer image to determine error values. The final discussion will involve several other examples of physical data used in computer imaging and modeling including topics from weather system animation to celestial mapping.

  28. Imaging A Section Of The Model This is a digital elevation model of the section of the model shown.

  29. Different Types Of Views Gray scale wire Grayscale

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