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May 2010


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"MOORE, TIMOTHY Mr CIV USA AMC" <[log in to unmask]>
Reply To:
Classification, clustering, and phylogeny estimation
Fri, 14 May 2010 17:58:31 -0400
text/plain (3146 bytes) , smime.p7s (10 kB)
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I thought you might be interested in the following: 

Image Change Detection Algorithms: A Systematic Survey


"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"


Some phrase I googled searched:

Near Sets
Color difference
Lab color space
Salient features
Change detection
Segmentation  image processing

Some website you might what to look at  (IEEE digital library) (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

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 <blocked>
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