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Re: Find Area of Fracture Surface Voids

  • Subject: Re: Find Area of Fracture Surface Voids
  • From: Roy Schestowitz <newsgroups@xxxxxxxxxxxxxxx>
  • Date: Mon, 07 Nov 2005 12:39:34 +0000
  • Newsgroups: comp.graphics.apps.gimp
  • Organization: schestowitz.com / MCC / Manchester University
  • References: <1131328729.653551.120700@f14g2000cwb.googlegroups.com> <KHAbf.817$Xo.706@newsread2.news.pas.earthlink.net> <1131342200.806305.107150@g47g2000cwa.googlegroups.com>
  • Reply-to: newsgroups@xxxxxxxxxxxxxxx
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__/ [derek] on Monday 07 November 2005 05:43 \__

> Dave:  I'm not sure if your question is directed at me or not... but I
> can tell you what the voids are, if you're interested.  The fracture
> surface that I mentioned above is of an adhesive of which we're trying
> to ascertain properties under dynamic load.  For whatever reason, one
> batch of the adhesive that we got does not perform in the same manner
> as a different batch, and the easiest explanation seems to be that
> somewhere in the packaging process (of the adhesive), there was a fair
> amount of air trapped inside of the stuff.  This causes, of course,
> 'voids' where there should have been adhesive when you look at the
> fracture surface.  Does that make any sense?

Not only does it makes sense, but it makes the disucssion a more interesting

You might also wish to try sci.image.processing where people can suggest
algorithms and recommend software packages.

> Roy:  Thanks much for the idea... sorry though, I'm not too familiar
> with available graphics programs... do you have any suggestions for
> something I might use?  It might take some fiddling to get it to work
> right, as there's a bit of black in the pictures that is not caused by
> the voids.  Of course, I'm not sure what 'shade' of black it is, so it
> may be that, like you said, I can pick out certain parts of the
> histogram that correspond to the particular colors that I find in the
> voids.
> Anyways, thanks for the responses!  I'll keep at it.
> ~Derke

If you have some 'interference' due to other smaller dark elements, try to
classify them. If you are going to deal with many such images, I also
suggest that you automate this. To classify based on size, try checking the
pixel neighbourhood and put a threshold on the number of neighbouring
pixels. If you save the image as a 24-bit (1 byte red, 1 byte green, 1 byte
blue) bitmap file, then you can easily scan the file and check the
thresholds while accumulating a count of dark pixels.


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