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Comet Detection and Tracing Algorithm using SOHO Images

We developed a MATLAB program that utilizes SOHO satellite images to detect comets by isolating white dots representing comets, noise, and stars against a black background. The program tracks dot movements, considers their paths, and filters out non-comet objects based on distance, angle, and brightness. The final algorithm successfully isolates and traces comets in a set of images.

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Comet Detection and Tracing Algorithm using SOHO Images

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  1. By Soham Chakraborty, Joey Huntley, and Susan Xie

  2. The Project • The premise was to use SI resources to locate comets in images taken from SOHO. • SOHO is a satellite that takes pictures of the sun. Noise, stars, and comets are visible in the background, but are not distinguishable from one another. • All code was to be written in MATLAB, a numerical computing environment and programming language.

  3. Original Image

  4. The Process • First, the images were loaded into MATLAB. • Next, the images were processed to show only white dots representing comets, noise, and stars against a black background. • Then, we found the coordinates of all the white dots in each image matrix, which consisted only of 0’s for black pixels and 1’s for white pixels.

  5. Processed Image

  6. The Process (2) • Next, we found the differences in time between subsequent images. These times varied, but were usually 20-40 minutes. • Since comets move at a constant speed, around 1 pixel per minute in our images, we were able to eliminate dots that didn’t fall in the range. We tracked dot movements and considered paths over groups of 4 images.

  7. The Process (3) • We found all the possible paths constrained by the distance requirements. • Next, we considered the angle over subsequent images. Comets will move in essentially straight lines, although some leniency was given. Paths that weren’t relatively straight were filtered out. • Since SOHO has a circular orbit, the dots that are stars will appear to move horizontally. All horizontally moving objects were removed.

  8. The Process (4) • The final consideration was brightness. Fake comet paths might be found between random noise dots. Since noise varies in intensity, and comets do not, we eliminated all dots that varied significantly in brightness. • MATLAB functions were created to perform each task. These were then packaged together with a master function and a program loop that only allowed a hundred images at a time to be loaded and processed.

  9. The Results • We ran the final function on a set of images known to contain comets. While it was not very efficient and had a long run time, our program was successful in isolating and tracing the comets. • Thanks again to our mighty project leader, Alan Chalker, the master of MATLAB, for all his help.

  10. THE VISUALIZATION • Using MATLAB, a series of animation frames were created by using program loops to essentially take a “shot” of the images in each step of the visualization. • This resulted in an animation with over 200 frames that displays the comets as they move across the image groups and green lines that trace their paths.

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