Subject: Re: Adaptive Thresholding and Segmentation
From: slackey@slackey-sun.lightstream.com (Stan Lackey)
Date: 13 Nov 1996 16:02:22 GMT
In article <01bbd0ad$93a67560$3650f6cc@KevinL.pptvision.com> "Kevin Landman" writes:
Another thing you might try is to analyze the histogram a little. If
the population is clumped, it means it's the bad case you cite. If
the histogram is mostly bimodal, it's the ideal case. If it's
trimodal or more, you'll need something else - don't ask me what.
Good luck!
The approach you are taking is reasonable...
Gord Bowman wrote in article
<01bbd018$5410cbc0$053ae9cd@gord.atlsci.com>...
> I'm trying to locate dark regions in an image using the adaptive
> thresholding technique, which I have heard to be useful for such an
> application. Being unable to find an actual description of this
algorithm,
> I assumed it to be:
>
> Run a moving window over an image. If the value of the centre pixel is
more
> than a specified threshold different from the average of the pixels in
the
> window, set it equal to 1, otherwise 0.
>
> The obvious problem I encountered with this was that if the dark region
or
> bright region fully encompasses the window, there is no way to
distinguish
> between them because the difference between the centre pixel value and
the
> mean is essentially zero.
> Unless I am totally missing something, I don't see how this could
possibly
> be a good algorithm for spot detection.
>
> My questions are:
> (1) Am I not understanding the adaptive thresholding algorithm?
> (2) Is what I have done still considered "adaptive" thresholding?
Subject: Announce: MAT C++ Matrix Math Library
From: info@mathtools.com (MathTools Info)
Date: Thu, 14 Nov 1996 09:40:30 GMT
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Subject: Re: snake control problem
From: william@cs.ust.hk (Cheung Kwok Wai)
Date: 14 Nov 1996 04:15:41 GMT
Haitao Jiang (jiang@cs.purdue.edu) wrote:
: Hi, everyone,
: Does anyone know how to fix the following problem of the snake model?
: I am using snake model to track a human being, and I am only interested
: in his/her head which can be an open snake, now since two end points are
: also moving, I lose control of the snake, they all shrink into a point,
: even I know how to prevent the shrinking tendency, they can still moving
: to another unwanted part. How to somehow control the snake to converge
: to the region that I initially interested in?
It's hard to say something unless ones know what formulation you are
using for the snake. Are you using Kass et al.'s formulation? If yes,
the tendency for the snake shrinking to a point is in fact inherited in
the formulation. Many researchers have proposed different
modifications and you can easily find them from the recent literature.
Other than the formulation, the smoothness of the snake can be controlled
by varying the weighting parameters of the two energy functions,
commonly called internal and external energies. We have done some work
in determining the optimal value by Bayesian inference. You can refer to
our ACCV95 paper in http://www.cs.ust.hk/~william/paper.html.
Hope this help.
William
Subject: Re: Downsampling without aliasing
From: Jerome Kalifa
Date: Thu, 14 Nov 1996 10:03:09 +0100
Pierre-Martin Tardif wrote:
>
> tsui@mhd1.pfc.mit.edu wrote:
>
> >Dear friends:
> >How to downsample an image without aliasing effect?
>
> You need to filter out 1/2 of the spectrum width. So, your
> bandpass need to be less or equal to 1/2. Usually,
> this filter is a low-pass filter.
>
> Ideal LPF:
> ^
> |
> |------------------|
> | |
> --+------------------|--------------------|-----> f
> 0 1/2 1
>
> PMT
>
>
Well...I think that, if you use such a filter, you will obtain gibbs
phenomena in the neighborhood of the singularities.
Ideally, you should use a filter whose cut-off is more *progressive* :
^
|
|--------------
| -
| -
| -
--+----------------|--------------------|-----> f
0 1/2 1
The functions whose frequential behaviour is considered as optimal (in
the sense that they are also highly concentrated in space) for this
application are the prolate spheroidal functions. I think that they are
described in Papoulis' book.
They work fine either for 1-d and 2-d problems.
--
Jerome Kalifa
Centre de Mathematiques Appliquees,
Ecole Polytechnique, 91128 Palaiseau Cedex,
France. (33)169333981
Subject: Re: Image Processing Books
From: Markus Ebbecke
Date: Thu, 14 Nov 1996 11:37:56 +0100
> Can anyone recommend some basic books on image processing? I'm
> trying to improve the quality of scanned black and white images
> prior to performing vector to raster conversion. As opposed to
> the complete bible of image processing I'm looking for something
> that lists the most common techniques and describes when they
> should be used. Some hints on coding would also be useful.
You can try the following very nice book:
Imaga Processing, Analysis and Machine Vision
Milan Sonka, Vaclac Hlavac, Roger Boyle
Chapman&Hall; Computing
ISBN 0-412-45570-6
It includes nearly everithing about image processing, is well
understandable, includes many algorithms in pseudo-code and has many
many references to start search for more detailed information.
For online-search try
http://www.ph.tn.tudelft.nl/bibliographic.html
Sincerely
Markus
--
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\
Markus Ebbecke
University of Kaiserslautern, Germany
ebbecke@student.uni-kl.de
Voice +49/631/13582
http://www.student.uni-kl.de/~ebbecke
Subject: saturation definition
From: Heeryung _ Kim
Date: Wed, 13 Nov 1996 18:02:40 -0500
Dear all here;
I am testing the saturation component images on the deifferent color
models such as HSV, HLS and ISH color model.
Why the definition of saturations are defferent?
S=1-3*min(R,G,B)/(R+G+B) on HLS
S= {max(R,G,B)-min(R,G,B)}/max(R,G,B) on HSV
and s is also different on the ISH color model.
What are these definition based on ?
I'll appreciate any comment for this.
Thanks
Subject: Employment: South Africa, Remote Sensing Researchers
From: chris@bayes.agric.za (Christopher Gordon)
Date: 14 Nov 1996 12:41:28 GMT
AGRICULTURAL RESEARCH COUNCIL of SOUTH AFRICA
INSTITUTE FOR SOIL CLIMATE AND WATER
REMOTE SENSING DIVISION
The following positions are now on offer at this Pretoria, South
Africa based Institute with its well equipped digital image
processing facility.
The successful candidates will form part of a team of 12
researchers and support staff specializing in Remote Sensing.
Three persons are required to research the development and
application of Remote Sensing Techniques for obtaining
Environmental and Agricultural Resource Information and
Statistics.
In addition to the educational requirements set for each
position, a relevant post graduate qualification and/or
experience in Remote Sensing/Digital Image Processing and GIS
will serve as a strong recommendation in each instance.
The specific requirements for each position are as follows:
Post 1 Rangeland Applications: A university degree in Ecology,
Botany, Rangelands Science or related fields.
Post 2 RADAR Applications: A university degree in Physics,
Applied Mathematics, Statistics, Engineering or a related field.
Post 3: A university degree in Natural, Earth or Pure Science or
related field (Soil Science, Geography, Botany, Geology,
Environmental Studies)
Applicants for all posts may be required to undertake
psychometric tests.
The ARC offers challenging opportunities in a pleasant work
environment as well as competitive remuneration packages,
including standard fringe benefits, which will be negotiated in
accordance with qualifications and experience.
Please forward your application together with CV to:
The Director:ISCW, P.Bag X79, Pretoria, 0001. (Fax --27 12 323
1157)
Applications close on 22 November 1996
Enquiries:
Dr JF Eloff / Mr TS Newby ph (--27 12) 326 4205
E-Mail : TERRY@IGKW2.AGRIC.ZA
Subject: Re: Television R,G,B pixel standard
From: Mika Iisakkila
Date: 14 Nov 1996 17:12:23 +0200
robgc@infomatch.com (Rob Chambers) writes:
> I am looking for information regarding the CIE value for NTSC and PAL
> television Red, Green, and Blue phosphors.
> I image it's somewhere around 470, 540, and 620 for NTSC (6500K) Is PAL based
> on a different colour temperature?
The exact values for NTSC white point are (x and y are CIE colour
coefficients, lambda is wavelength):
colour lambda x y T
C - 0.31 0.316 6735K
Rc 619nm 0.67 0.33
Gc 535nm 0.21 0.71
Bc 470nm 0.14 0.08
The new white point standard as recommended by EBU (I suppose modern
PAL equipment uses these colours) is:
colour lambda x y T
W - 0.281 0.311 8500K
Rw 610nm 0.64 0.34
Gw 540nm 0.27 0.59
Bw 465nm 0.15 0.07
--
comfortably numb