Image Analysis - second order moments - ellipticities

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SUMMARY

This discussion focuses on calculating ellipticities and orientation angles of images using their second order moments of inertia. The user seeks clarity on the summation process involved in this calculation, specifically regarding the use of x and y coordinates derived from the matrix representation of greyscale images. It is emphasized that defining the region of interest is crucial, which can be achieved through conditions such as grayscale intensity thresholds to isolate specific features within the image.

PREREQUISITES
  • Understanding of second order moments of inertia in image analysis
  • Familiarity with matrix representations of images
  • Knowledge of grayscale intensity thresholds for image segmentation
  • Basic concepts of ellipticities and orientation angles in image processing
NEXT STEPS
  • Research the mathematical formulation of second order moments of inertia in image analysis
  • Learn how to implement image segmentation techniques using grayscale intensity thresholds
  • Explore algorithms for calculating ellipticities and orientation angles from image data
  • Investigate software tools like OpenCV for practical image processing applications
USEFUL FOR

This discussion is beneficial for image analysts, computer vision researchers, and developers working on image processing applications who need to understand the calculation of ellipticities and orientation angles from second order moments of inertia.

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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