| Classification of objects in 2D image [message #22764] |
Fri, 01 December 2000 00:00  |
hahn
Messages: 108
Registered: November 1993
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Senior Member |
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Hi,
I'm looking for a tool box to classify objects that
occur in 2D images (TV quality). This could be part
of biological cells as well as crystall structures.
A public domain supplement for IDL would be most
welcome.
Thanks for any replies...
Norbert
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| Re: Classification of objects in 2D image [message #22992 is a reply to message #22764] |
Mon, 18 December 2000 17:46  |
Ben Tupper
Messages: 186
Registered: August 1999
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Senior Member |
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Hello,
Yes, Sobel filters and a lengthy list of other techniques are used. Which tool to use depends upon the
quality of you image (signal/noise) and what you hope to pull out of the image. I have been astounded
by the number of different techniques used (most of which sail right over me.) In this case, it sounds
to me like Nobert has the image filtered to the point where features of interest stand out from the
background. Now what is needed is a routine to make pretty much standard measurements of features size,
orientation relative to horizontal, etc. Of course, I could have missed the boat and misread Nobert's
needs.
Ben
Paul van Delst wrote:
>
>
> I thought that's what Sobel filters were for? Finding edges of features in images and such. An image
> consisting of crystals or crystal aggregates/clumps on a uniform background should show up well
> under edge enhancement.
>
> paulv
>
> --
> Paul van Delst Ph: (301) 763-8000 x7274
> CIMSS @ NOAA/NCEP Fax: (301) 763-8545
> Rm.207, 5200 Auth Rd. Email: pvandelst@ncep.noaa.gov
> Camp Springs MD 20746
--
Ben Tupper
248 Lower Round Pond Road
POB 106
Bristol, ME 04539
Tel: (207) 563-1048
Email: PemaquidRiver@tidewater.net
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| Re: Classification of objects in 2D image [message #23000 is a reply to message #22764] |
Mon, 18 December 2000 09:04 |
Paul van Delst
Messages: 364
Registered: March 1997
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Senior Member |
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Ben Tupper wrote:
>
> Hello,
>
> If I understand you correctly, you have the the image segemented
> into 'features' and 'background'. I do have an object (still in
> the development stage that will spit out 'feature'
> characteristics given a segemented image with only one
> 'feature'. I don't have it handy here, but I could send it to
> you soon.
>
> One limitation it has (a least a limitation for me) is a 'good'
> way of finding the perimeter of the feature. I use the CONTOUR
> procedure on a binary version of the image. The results are OK
> for me, but IDL may not be calculating that perimeter the way you
> would want.
I thought that's what Sobel filters were for? Finding edges of features in images and such. An image
consisting of crystals or crystal aggregates/clumps on a uniform background should show up well
under edge enhancement.
paulv
--
Paul van Delst Ph: (301) 763-8000 x7274
CIMSS @ NOAA/NCEP Fax: (301) 763-8545
Rm.207, 5200 Auth Rd. Email: pvandelst@ncep.noaa.gov
Camp Springs MD 20746
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| Re: Classification of objects in 2D image [message #23002 is a reply to message #22764] |
Mon, 18 December 2000 06:33 |
Ben Tupper
Messages: 186
Registered: August 1999
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Senior Member |
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Hello,
If I understand you correctly, you have the the image segemented
into 'features' and 'background'. I do have an object (still in
the development stage that will spit out 'feature'
characteristics given a segemented image with only one
'feature'. I don't have it handy here, but I could send it to
you soon.
One limitation it has (a least a limitation for me) is a 'good'
way of finding the perimeter of the feature. I use the CONTOUR
procedure on a binary version of the image. The results are OK
for me, but IDL may not be calculating that perimeter the way you
would want.
Ben
Norbert Hahn wrote:
> Ben Tupper <pemaquidriver@tidewater.net> wrote:
>
>> Hello,
>>
>> Could you describe the images you have in hand? I have a
>> number of thought, but it helps to know where your staring
>> point is.
>
> The images are greyscale images of crystals viewed under
> a microscope. This is an attempt to classify the quality
> of food by dissolving some salt in milk and evaporating
> the liquid. Other components are removed too until the
> salt crystals are left over.
>>
>> Here are some questions that might determine the approach
>> you take:
>> -Are the images of 'natural' samples with lots of
>> detritus/junk floating around?
>
> There is little junk floating around when the experiment
> is correctly done.
>
>> -Is the background uniform or varying?
>
> The background is uniform in a single picture but varies
> from picture to picture. The threshold has to be adjusted
> for each picture, but that seems to be manageable.
>
>> -Are the features detectable with a simple threshold
>> applied to the entire image, or do you need to consider
>> regional thresholding?
>
> A simple threshold for the entire image should be sufficient.
>
>> -Do you have only one image per field of view or do you
>> have multiple images for each field of view?
>
> There is only one image per sample of the fluid under test.
>
> One of the major problems is that the crystals have a
> varying orientation from picture to picture. Other
> properties such a shape, size and clustering vary little
> within one class. So variations of shape, size and
> clusterings should be detected and grouped into various
> classes.
>
> Norbert
--
Ben Tupper
248 Lower Round Pond Road
POB 106
Bristol, ME 04539
Tel: (207) 563-1048
Email: PemaquidRiver@tidewater.net
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| Re: Classification of objects in 2D image [message #23017 is a reply to message #22764] |
Fri, 15 December 2000 06:24 |
hahn
Messages: 108
Registered: November 1993
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Senior Member |
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Ben Tupper <pemaquidriver@tidewater.net> wrote:
> Hello,
>
> Could you describe the images you have in hand? I have a
> number of thought, but it helps to know where your staring
> point is.
The images are greyscale images of crystals viewed under
a microscope. This is an attempt to classify the quality
of food by dissolving some salt in milk and evaporating
the liquid. Other components are removed too until the
salt crystals are left over.
>
> Here are some questions that might determine the approach
> you take:
> -Are the images of 'natural' samples with lots of
> detritus/junk floating around?
There is little junk floating around when the experiment
is correctly done.
> -Is the background uniform or varying?
The background is uniform in a single picture but varies
from picture to picture. The threshold has to be adjusted
for each picture, but that seems to be manageable.
> -Are the features detectable with a simple threshold
> applied to the entire image, or do you need to consider
> regional thresholding?
A simple threshold for the entire image should be sufficient.
> -Do you have only one image per field of view or do you
> have multiple images for each field of view?
There is only one image per sample of the fluid under test.
One of the major problems is that the crystals have a
varying orientation from picture to picture. Other
properties such a shape, size and clustering vary little
within one class. So variations of shape, size and
clusterings should be detected and grouped into various
classes.
Norbert
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