Scientific Visualization

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Scientific Visualization - PowerPoint PPT Presentation

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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PowerPoint Slideshow about 'Scientific Visualization' - ciro

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Presentation Transcript
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
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
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)

The Final Picture

The Original Array

The Color Pallete

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
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
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
Rainbow vs. Banded Palette

These are two different ways of viewing the same data

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
• 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
Interpolated Image

Raw Image

Advertise what data manipulation you have done, and be careful not to introduce artifacts into your data

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

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
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
The Dataset

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

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

The Data Array

These numbers represent the heights of a part of the model

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.

Imaging A Section Of The Model

This is a digital elevation model of the section of the model shown.

Different Types Of Views

Gray scale wire

Grayscale