Department of Electronics and Communication Engineering
School of Engineering and Technology
Digital Image Processing
CIA-1_COMPONENT-1
Report
Submitted by
Parthasarathy M
(2062629)
from 7BTELCS
Of
B. Tech Electronics and Computer Engineering [with
specialization in AI &ML]
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Extraction of properties of stars in an
astronomical image
Aim
The aim of this project is to detect and measure the properties of stars in an astronomical
image.
Objective:
The objective is to implement an image processing technique using MATLAB that can
identify and mark the stars in an image. Further, it aims to extract and display the properties
of these detected stars, such as their locations and areas.
Tool:
For my image processing project, I utilized MATLAB Online, which provided a convenient
and accessible software environment for my work.
Methodology
The methodology involves several steps:
1. Image Acquisition: This involves loading an astronomical image into the MATLAB
environment for processing.
2. Preprocessing: If the image is in color, it's converted to grayscale since color information
is not necessary for star detection in this scenario.
3. Normalization: The grayscale image is converted to double precision and normalized to a
range between 0 and 1. This enhances contrast and ensures consistent processing regardless
of the original image's scale.
4. Star Detection: The processed image is subjected to a local maximum detection operation.
This operation identifies all local maxima in the image, which are likely candidates for stars,
given that stars are generally brighter than their immediate surroundings.
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5. Measurement: The connected components (potential stars) are measured for their
properties. The properties extracted include their central coordinates (Centroid), areas, and
perimeters.
6. Display The original image is displayed with the detected stars marked on it. This serves
as a visual confirmation of the star detection process.
MATLAB Script
%Image Processing toolbox installed in MATLAB.
% First, read an astronomical image
img = imread('stars.png');
% Convert to grayscale image if it's not
if size(img, 3) == 3
img = rgb2gray(img);
end
% Display the image
figure, imshow(img), title('Original Image');
% Convert the image to double precision, and normalize it
img = im2double(img);
img = (img - min(img(:))) / (max(img(:)) - min(img(:)));
% Find local maxima (stars should appear as local maxima)
localMax = imextendedmax(img, 0.5); % Adjust the 0.5 threshold as needed
% Display the local maxima
figure, imshow(localMax), title('Local Maxima');
% Measure properties of the connected components (the stars)
cc = bwconncomp(localMax);
stats = regionprops('table',cc,'Centroid','Area','Perimeter');
% Display the centroid locations of the detected stars
figure, imshow(img), hold on, title('Detected Stars');
scatter(stats.Centroid(:,1), stats.Centroid(:,2), 'r');
Result
We can identify the stars in an astronomical image and extract their properties. The
effectiveness of this method may vary depending on the quality and characteristics of the
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input image. For complex images or advanced applications, more sophisticated image
processing techniques may be required.
Conclusion
Image processing is a powerful tool in astronomy, enabling automated analysis of vast
amounts of data. While this project presents a simple example, similar techniques are used
in real-world applications, often with much more complexity and sophistication. As
astronomical imaging technology continues to advance, image processing techniques will
continue to play a crucial role in unlocking new discoveries about the universe.
Input:
The input would be an astronomical image, ideally containing numerous stars against a
dark background. For example, you might have a black and white image of a portion of the
night sky, filled with tiny white dots representing various stars and other celestial bodies.
Output:
The output would be the same image with the detected stars highlighted. The highlighting
could be done in various ways—in this case, the code specifies that detected stars are
marked with red dots at their centroids. So, your output would be the same black and white
image of the night sky, but with red dots marking the identified stars.
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