| Re: Determining circularity [message #25175] |
Fri, 25 May 2001 19:04  |
Craig Markwardt
Messages: 1869
Registered: November 1996
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Senior Member |
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Todd Clements <mole6e23@hotmail.com> writes:
> Hi all...
>
> I was wondering if anyone knew of routines to determine the circularity
> of an image. We have images from our detector and for optimal
> calibration, they need to be as circular as possible, and it's difficult
> to tell by eye when it's close but not quite there.
>
> Generally these images have fairly well defined edges, although there is
> definitely some gray area as to what consistutes the edge, which makes
> it difficult to determine by eye sometimes.
>
> Basically, I guess it comes down to fitting the edge of the image to an
> ellipse, but there has to be some determination of what the edge is as
> well.
Possible solution:
1. Edge filter with ROBERTSON (or perhaps SOBEL)
2. Extract X- and Y-position of edge points (use a threshold)
3. Fit to an ellipse using MPFITELLIPSE
The last one is from my web page, and can be used for fitting an
ellipse to a set of scattered points. It's not theoretically perfect
but is good for rough calcs. Of course you need MPFIT as well. Here
is a sample go-round. First I construct some fake data and then
perform the steps above.
;; Construct a filled circle with a radius of 6
x = findgen(101)*0.2 - 10. & y = x
xx = x # (y *0 + 1) ;; Construct X, Y, and R (radius) values
yy = (x*0+1) # y
rr = sqrt(xx^2 + yy^2)
wh = where(rr LT 6.) ;; Fill the circle
im = xx*0
im(wh) = 1
;; Step 1. Extract edge-filtered image
edge = roberts(im)
;; Step 2. Extract edge points using threshold value (value of 2 here)
wh = where(edge EQ 2)
xim = xx(wh) & yim = yy(wh)
;; Step 3. Fit the image
print, mpfitellipse(xim, yim)
... results are ...
5.96942 5.96942 -0.100046 -0.100019 0.00000
XSEMI YSEMI XCENTER YCENTER ROTATION = 0
I make it look easy. The real trick is to find the right threshold to
select the points from the data, and filtering out any other noise
which will surely screw up the edge enhancement.
Craig
------------------------------------------------------------ --------------
Craig B. Markwardt, Ph.D. EMAIL: craigmnet@cow.physics.wisc.edu
Astrophysics, IDL, Finance, Derivatives | Remove "net" for better response
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| Re: Determining circularity [message #25251 is a reply to message #25175] |
Fri, 01 June 2001 09:22  |
Vince Hradil
Messages: 574
Registered: December 1999
|
Senior Member |
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May be simpler to use this definition of circularity: the variance of the
boundary pixels distance to the centroid.
1- compute centroid, (xc,yc): xc=sum(x)/area
2- find the boundary pixels
3- calcuate the mean distance from the centroid to the the boundary pixels
and the variance
4- circularity =variance/mean (circles are 0)
"Craig Markwardt" <craigmnet@cow.physics.wisc.edu> wrote in message
news:on8zjkeuhj.fsf@cow.physics.wisc.edu...
>
> Todd Clements <mole6e23@hotmail.com> writes:
>
>> Hi all...
>>
>> I was wondering if anyone knew of routines to determine the circularity
>> of an image. We have images from our detector and for optimal
>> calibration, they need to be as circular as possible, and it's difficult
>> to tell by eye when it's close but not quite there.
>>
>> Generally these images have fairly well defined edges, although there is
>> definitely some gray area as to what consistutes the edge, which makes
>> it difficult to determine by eye sometimes.
>>
>> Basically, I guess it comes down to fitting the edge of the image to an
>> ellipse, but there has to be some determination of what the edge is as
>> well.
>
> Possible solution:
>
> 1. Edge filter with ROBERTSON (or perhaps SOBEL)
> 2. Extract X- and Y-position of edge points (use a threshold)
> 3. Fit to an ellipse using MPFITELLIPSE
>
> The last one is from my web page, and can be used for fitting an
> ellipse to a set of scattered points. It's not theoretically perfect
> but is good for rough calcs. Of course you need MPFIT as well. Here
> is a sample go-round. First I construct some fake data and then
> perform the steps above.
>
> ;; Construct a filled circle with a radius of 6
> x = findgen(101)*0.2 - 10. & y = x
> xx = x # (y *0 + 1) ;; Construct X, Y, and R (radius) values
> yy = (x*0+1) # y
> rr = sqrt(xx^2 + yy^2)
> wh = where(rr LT 6.) ;; Fill the circle
> im = xx*0
> im(wh) = 1
>
> ;; Step 1. Extract edge-filtered image
> edge = roberts(im)
>
> ;; Step 2. Extract edge points using threshold value (value of 2 here)
> wh = where(edge EQ 2)
> xim = xx(wh) & yim = yy(wh)
>
> ;; Step 3. Fit the image
> print, mpfitellipse(xim, yim)
> ... results are ...
> 5.96942 5.96942 -0.100046 -0.100019 0.00000
> XSEMI YSEMI XCENTER YCENTER ROTATION = 0
>
> I make it look easy. The real trick is to find the right threshold to
> select the points from the data, and filtering out any other noise
> which will surely screw up the edge enhancement.
>
> Craig
>
> ------------------------------------------------------------ --------------
> Craig B. Markwardt, Ph.D. EMAIL: craigmnet@cow.physics.wisc.edu
> Astrophysics, IDL, Finance, Derivatives | Remove "net" for better response
> ------------------------------------------------------------ --------------
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