Image Analysis - second order moments - ellipticities

AI Thread Summary
The discussion centers on calculating ellipticities and orientation angles of images using their second order moments of inertia. The user seeks clarification on performing the summation involved in this calculation, specifically how to define the x and y values. It is noted that x and y represent the coordinates of each point within the image matrix, corresponding to the indices of the matrix elements (column and row). A crucial step is defining the region of interest within the image, which can be achieved by setting a condition, such as a grayscale intensity threshold, to isolate specific features like bright spots. The provided source offers some guidance, but the user remains uncertain about the practical application of the summation process.
Miss_Astro
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There seems to be a good source here:

http://www.cs.cf.ac.uk/Dave/AI2/node194.html


But my problem is understanding the summation. What I am trying to do is calculate ellipticites and orientation angles of images using their second order moments of inertia.


My images are in matrix form with values depicting the greyscale of the image. Do you know what I would actually do? The above source is helpful but I am stuck on the actual summation and what would be use as x and y values.
 
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x and y are the coordinates of each point within the region, so in your case it would be the index of the matrix elements (column, row).

But first you have to define the region that is part of the object. You have to set some kind of condition that defines the region. For example, it could be a grayscale intensity threshold if you are looking for bright spots.
 

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