Moments of inertia in image processing

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Discussion Overview

The discussion revolves around the calculation of moments of inertia in the context of image processing, specifically for distinguishing fibers from noise in grayscale pixel data. Participants explore the application of physics concepts to analyze image characteristics and the quantification of pixel "blob-iness" using eigenvalues of an inertial matrix.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant seeks to calculate the moment of inertia for grayscale pixels, emphasizing the need to account for non-uniform weights based on pixel intensity.
  • Another participant expresses confusion regarding the application of moments of inertia to imaging, questioning the context of "gray scale" and whether it pertains to electron microscopy.
  • A participant suggests that the analysis is standard and mentions that existing software like MatLab and ImageJ have routines for similar calculations, noting that the eigenvalues represent the "principal axes" of the blob.
  • A later reply reiterates the standard nature of the analysis and provides a link to a resource on image moments, while also inquiring about specific functions in MatLab's Image Processing toolkit.
  • Another participant points out the 'regionprops' tool in MatLab as a potential solution for the problem at hand.

Areas of Agreement / Disagreement

Participants express varying levels of familiarity with the topic, and while some agree on the standard nature of the analysis, there is no consensus on the specific methods or tools available for implementation in MatLab.

Contextual Notes

There are unresolved questions regarding the precise definitions and assumptions related to the moment of inertia in this context, as well as the applicability of certain software functions.

psycovic23
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Hi,

I'm currently working on an imaging problem that oddly requires some physics. Basically, I'm given a set of gray scale pixels and I have to determine whether they're a fiber or just random noise. My question is, how do I calculate the moment of inertia of the pixels while considering their gray scale value? I know how to calculate a uniform moment of inertia (\sum r^{2}), but not when I have to consider discrete, non uniform weights to each pixel.

A continuation of that part is, how might I quantize the "blob-iness" of the pixels? A professor suggested finding the eigenvalues of the inertial matrix [m_x m_xy; m_xy m_y], but I'm not entirely sure what the eigenvalues end up representing. Any ideas?

Thanks!
 
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I found this to be a very confusing question with regards to the moment of inertia and imaging until I pondered "grey scale"

Are you referring to an electron microscope?
 
What you are trying to do is a fairly standard analysis, and many programs out there (MatLab, ImageJ, etc) have routines for this already. Essentially, you are weighting the calculation by intensity rather than mass, but the idea is the same, and the eigenvalues are the "principal axes", if you like, of the blob.

http://en.wikipedia.org/wiki/Image_moments
 
Andy Resnick said:
What you are trying to do is a fairly standard analysis, and many programs out there (MatLab, ImageJ, etc) have routines for this already. Essentially, you are weighting the calculation by intensity rather than mass, but the idea is the same, and the eigenvalues are the "principal axes", if you like, of the blob.

http://en.wikipedia.org/wiki/Image_moments

I'm using Matlab, but have been unable to find any functions to do it. Are there functions in the Image Processing kit that I'm missing?
 
I'm surprised there's not an obvious choice... the 'regionprops' tool has a few things that may work for you.
 

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