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May 2010

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"Classification, clustering, and phylogeny estimation" <[log in to unmask]>
Date:
Mon, 17 May 2010 08:57:11 +0200
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"Classification, clustering, and phylogeny estimation" <[log in to unmask]>
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From:
Yakir Gagnon <[log in to unmask]>
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Thank you very much! I'll be sure to use some of those links for more than
just distance between pixel intensities...
I've decided to sample my image in specific places where a difference in
pixel intensities would be more meaningful. Since each site I sample at is
supposed to be fairly independent of each other I'm using euclidean
distance.
Thanks for all the help!!!

Yakir L. Gagnon, PhD student
The Lund Vision Group
Tel  +46 (046) 222 93 40
Cell +46 (073) 753 63 54
Fax +46 (046) 222 44 25
http://www.lu.se/o.o.i.s/7758
http://www.google.com/profiles/12.yakir


On 14 May 2010 23:58, MOORE, TIMOTHY Mr CIV USA AMC <
[log in to unmask]> wrote:

> Classification: UNCLASSIFIED
> Caveats: NONE
>
> I thought you might be interested in the following:
>
>
>
> Image Change Detection Algorithms: A Systematic Survey
>
> www.ecse.rpi.edu/~roysam/PDF/J51.pdf
>
> ======================
>
>
> "Near set theory provides methods that can be used to extract resemblance
> information from objects contained in disjoint sets, i.e., it provides a
> formal basis for the observation, comparison, and classification of
> objects.
> The discovery of near sets begins with choosing the appropriate method to
> describe observed objects. For example, collections of digital images
> viewed
> as disjoint sets of points provide a rich hunting ground for near sets."
>
> <...>
>
> "The Near set Evaluation and Recognition (NEAR) system, is a system
> developed to demonstrate practical applications of near set theory to the
> problems of image segmentation evaluation and image correspondence. It was
> motivated by a need for a freely available software tool that can provide
> results for research and to generate interest in near set theory"
>
> http://en.wikipedia.org/wiki/Near_sets
>
> =======
>
> Some phrase I googled searched:
>
>
> Near Sets
> Psychophysics
> Color difference
> Lab color space
> Salient features
> Change detection
> Segmentation  image processing
>
>
> Some website you might what to look at
> www.visionbib.com
>
> ieexplore.ieee.org  (IEEE digital library)
> portal.acm.org (ACM digital library)
>
>
>
>
>
> -----Original Message-----
> From: Classification, clustering, and phylogeny estimation
> [mailto:[log in to unmask]] On Behalf Of Yakir Gagnon
> Sent: Friday, May 14, 2010 11:35 AM
> To: [log in to unmask]
> Subject: A good measure of distance for pixel intensities
>
> Hi!
> Background: I want to compare two images that are identical in many
> respects
> (pixel-wise) but are different in the pixel intensity values they have. I'm
> aware of the many image comparison methods out there, but I want to keep it
> very very simple  (for all the image analysists: these two images are
> artificial and made through the same process with some differences without
> any spatial translation).
> What I am doing right now: is calculating the euclidian distance between
> the
> pixel intensities of the 2 images. The mean of all the distances (if the
> image is 500*500 pixels then there are 500^2 distances) gives me a measure
> of how similar those images are.
> My question is: is the euclidian distance really the best option when
> comparing natural numbers that can only range between 0 and 255, or should
> I
> use some other measure of distance or transform the pixel intensities
> first?
>
> Thanks in advance!
>
>
> Yakir L. Gagnon, PhD student
> The Lund Vision Group
> Tel  +46 (046) 222 93 40
> Cell +46 (073) 753 63 54
> Fax +46 (046) 222 44 25
> http://www.lu.se/o.o.i.s/7758 <blockedhttp://www.lu.se/o.o.i.s/7758>
> http://www.google.com/profiles/12.yakir
> <blockedhttp://www.google.com/profiles/12.yakir>
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> Classification: UNCLASSIFIED
> Caveats: NONE
>
>
>
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