Image Analysis - second order moments - ellipticities

In summary, The conversation discusses using second order moments of inertia to calculate ellipticities and orientation angles of images, which are in matrix form with values representing grayscale. The source provided is helpful, but the speaker is unclear on what to use as the x and y values for the summation and how to define the region of the object within the image.
  • #1
Miss_Astro
15
0
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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  • #2
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.
 

1. What is Image Analysis?

Image Analysis is the process of extracting meaningful information from digital images. This involves using mathematical and computational techniques to analyze the characteristics and patterns present in an image.

2. What are Second Order Moments in Image Analysis?

Second Order Moments refer to statistical measures used to describe the shape and orientation of an image. In image analysis, second order moments are used to calculate ellipticities, which describe the elongation and orientation of an object within an image.

3. What is the significance of Second Order Moments in Image Analysis?

Second Order Moments are important in image analysis because they provide quantitative measures of shape, size, and orientation of objects within an image. This information can be used to classify and identify objects, as well as detect changes or anomalies in the image.

4. How are Ellipticities calculated in Image Analysis?

Ellipticities are calculated using the second order moments of an image. This involves computing the central moments of the image, which are then used to calculate the eigenvalues and eigenvectors of the image's moment matrix. The ellipticity is then determined by the ratio of the eigenvalues.

5. What are some applications of Ellipticities in Image Analysis?

Ellipticities have a wide range of applications in image analysis, including object detection and recognition, shape analysis, and image registration. They are also used in medical imaging for analyzing the shape and size of tumors, and in astronomy for measuring the ellipticity of galaxies.

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