Looking for applications for this math idea
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| Looking for applications for this math idea | Ben Thurston | 27 Feb 20:56 |
| Looking for applications for this math idea | Ben Thurston | 27 Feb 20:56 |
| Looking for applications for this math idea | Ben Thurston | 27 Feb 21:09 |
| Looking for applications for this math idea | Nathan Summers | 28 Feb 04:29 |
| Looking for applications for this math idea | Bill Skaggs | 28 Feb 13:54 |
| Looking for applications for this math idea | Nathan Summers | 28 Feb 16:03 |
| Looking for applications for this math idea | jcupitt@gmail.com | 28 Feb 16:37 |
Looking for applications for this math idea
I developed this type of function that I feel is sort of like the statistical analogue of the Fourrier series, it breaks a distribution up into simple normal distribution components as the Fourrier series breaks a wave into simple sine wave components. I thought maybe there could be an application for it in image processing but I don't know enough about image processing to figure out how it would apply... Anyone have any ideas?
Looking for applications for this math idea
Sorry of course you need the link: http://benpaulthurstonblog.blogspot.com/2015/02/supposing-you-have-process-that-reaches.html
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston wrote:
I developed this type of function that I feel is sort of like the statistical analogue of the Fourrier series, it breaks a distribution up into simple normal distribution components as the Fourrier series breaks a wave into simple sine wave components. I thought maybe there could be an application for it in image processing but I don't know enough about image processing to figure out how it would apply... Anyone have any ideas?
Looking for applications for this math idea
Well I guess I should add that it also interpolates the known points of a distribution...
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston wrote:
Sorry of course you need the link:
http://benpaulthurstonblog.blogspot.com/2015/02/supposing-you-have-process-that-reaches.html
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston wrote:
I developed this type of function that I feel is sort of like the statistical analogue of the Fourrier series, it breaks a distribution up into simple normal distribution components as the Fourrier series breaks a wave into simple sine wave components. I thought maybe there could be an application for it in image processing but I don't know enough about image processing to figure out how it would apply... Anyone have any ideas?
Looking for applications for this math idea
The first thing that comes to mind is that it might be a good method for resizing images.
Rockwalrus
On Fri, Feb 27, 2015, 4:10 PM Ben Thurston wrote:
Well I guess I should add that it also interpolates the known points of a distribution...
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston wrote:
Sorry of course you need the link:
supposing-you-have-process-that-reaches.html
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston
wrote:
I developed this type of function that I feel is sort of like the statistical analogue of the Fourrier series, it breaks a distribution up into simple normal distribution components as the Fourrier series
breaks a
wave into simple sine wave components. I thought maybe there could be an application for it in image processing but I don't know enough about
image
processing to figure out how it would apply... Anyone have any ideas?
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Looking for applications for this math idea
Decomposing a distribution into Gaussians is the essence of unblurring. A good algorithm for doing that would of course be very useful, but there is an enormous literature on the topic, and the most important fact about it is that it is mathematically ill-posed. In other words, unless you add extra constraints, tiny changes in the source distribution result in very large changes in the output. (In image-processing terms, transforms of that type tend to create large artifacts.) Unless the new method has some way of handling that problem, it probably isn't going to be useful.
Bill
On Fri, Feb 27, 2015 at 10:29 PM, Nathan Summers wrote:
The first thing that comes to mind is that it might be a good method for resizing images.
Rockwalrus
On Fri, Feb 27, 2015, 4:10 PM Ben Thurston wrote:
Well I guess I should add that it also interpolates the known points of a distribution...
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston
wrote:
Sorry of course you need the link:
supposing-you-have-process-that-reaches.html
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston <
wrote:
I developed this type of function that I feel is sort of like the statistical analogue of the Fourrier series, it breaks a distribution
up
into simple normal distribution components as the Fourrier series
breaks a
wave into simple sine wave components. I thought maybe there could be
an
application for it in image processing but I don't know enough about
image
processing to figure out how it would apply... Anyone have any ideas?
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List archives: https://mail.gnome.org/archives/gimp-developer-list_______________________________________________ gimp-developer-list mailing list
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Looking for applications for this math idea
Good points.
Rockwalrus
On Sat, Feb 28, 2015, 8:54 AM Bill Skaggs wrote:
Decomposing a distribution into Gaussians is the essence of unblurring. A good algorithm for doing that would of course be very useful, but there is an enormous literature on the topic, and the most important fact about it is that it is mathematically ill-posed. In other words, unless you add extra constraints, tiny changes in the source distribution result in very large changes in the output. (In image-processing terms, transforms of that type tend to create large artifacts.) Unless the new method has some way of handling that problem, it probably isn't going to be useful.
Bill
On Fri, Feb 27, 2015 at 10:29 PM, Nathan Summers wrote:
The first thing that comes to mind is that it might be a good method for resizing images.
Rockwalrus
On Fri, Feb 27, 2015, 4:10 PM Ben Thurston wrote:
Well I guess I should add that it also interpolates the known points
of a
distribution...
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston <
wrote:
Sorry of course you need the link:
supposing-you-have-process-that-reaches.html
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston <
wrote:
I developed this type of function that I feel is sort of like the statistical analogue of the Fourrier series, it breaks a
distribution
up
into simple normal distribution components as the Fourrier series
breaks a
wave into simple sine wave components. I thought maybe there could
be
an
application for it in image processing but I don't know enough about
image
processing to figure out how it would apply... Anyone have any
ideas?
_______________________________________________ gimp-developer-list mailing list
List address: gimp-developer-list@gnome.org List membership: https://mail.gnome.org/mailman/listinfo/gimp- developer-list
List archives: https://mail.gnome.org/archives/gimp-developer-list_______________________________________________ gimp-developer-list mailing list
List address: gimp-developer-list@gnome.org List membership:
https://mail.gnome.org/mailman/listinfo/gimp-developer-list List archives: https://mail.gnome.org/archives/gimp-developer-list_______________________________________________ gimp-developer-list mailing list
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List archives: https://mail.gnome.org/archives/gimp-developer-list
Looking for applications for this math idea
Denoising is another obvious application. You could perhaps do your decomposition at various points over the image surface and see if you can find some noise function that's present everywhere and that therefore comes from the sensor rather than the object.
Again, there is a huge literature and it's a difficult problem.
http://en.wikipedia.org/wiki/Noise_reduction
On 28 February 2015 at 13:54, Bill Skaggs wrote:
Decomposing a distribution into Gaussians is the essence of unblurring. A good algorithm for doing that would of course be very useful, but there is an enormous literature on the topic, and the most important fact about it is that it is mathematically ill-posed. In other words, unless you add extra constraints, tiny changes in the source distribution result in very large changes in the output. (In image-processing terms, transforms of that type tend to create large artifacts.) Unless the new method has some way of handling that problem, it probably isn't going to be useful.
supposing-you-have-process-that-reaches.html
On Fri, Feb 27, 2015 at 3:56 PM, Ben Thurston <
wrote:
I developed this type of function that I feel is sort of like the statistical analogue of the Fourrier series, it breaks a distribution
up
into simple normal distribution components as the Fourrier series
breaks a
wave into simple sine wave components. I thought maybe there could be
an
application for it in image processing but I don't know enough about
image
processing to figure out how it would apply... Anyone have any ideas?
