[SciPy-user] Fitting sphere to 3d data points

James Vincent jjv5 at nih.gov
Thu Jan 25 09:53:56 CST 2007


Thanks for all the input. I think I've got it. This works:


def resSphere(p,x,y,z):
    """ residuals from sphere fit """

    a,b,c,r = p                             # a,b,c are center x,y,c  
coords to be fit, r is the radius to be fit
    distance = sqrt( (x-a)**2 + (y-b)**2 + (z-c)**2 )
    err = distance - r                 # err is distance from input  
point to current fitted surface

    return err

params = [0.,0.,0.,0.]
myResult = leastsq(resSphere, params, args=(myX,myY,myZ) )
print myResult[0]




On Jan 25, 2007, at 10:47 AM, David Douard wrote:

> On Thu, Jan 25, 2007 at 09:31:10AM -0500, David Huard wrote:
>> Hi James,
>>
>> As a first guess, I'd say the center of the sphere is simply the  
>> mean of
>> your data points, if they're all weighted equally.
>
> Hello,
>
> I would have rather said that you need to find the point that minimize
> the distance to the normals of the triangles you have from your data
> points (not sure this is really meaningful...).
> If the points really are on a sphere, all the normal will cut on one
> point. If not, there really is a minimization problema to solve.
>
> David
>
>
>
>> With only one parameter
>> left to fit, it should be easy enough. However, you may want to  
>> look at the
>> paper:
>>
>> Werman, Michael and Keren, Daniel
>> A Bayesian method for fitting parametric and nonparametric models  
>> to noisy
>> data
>> Ieee Transactions on Pattern Analysis and Machine Intelligence,  
>> 23, 2001.
>>
>> They write that the Mean Square Error approach overestimates the  
>> radius in
>> the case of circles. They don't talk about the 3D case, but I'd guess
>> similar problems arise. They provide a method to fit parametric  
>> shapes with
>> some robustness to data errors.
>>
>> Cheers,
>>
>> David
>>
>>
>>
>> 2007/1/25, James Vincent <jjv5 at nih.gov>:
>>>
>>> Hello,
>>> Is it possible to fit a sphere to 3D data points using
>>> scipy.optimize.leastsq? I'd like to minimize the residual for the  
>>> distance
>>> from the actual x,y,z point and the fitted sphere surface. I can  
>>> see how to
>>> minimize for z, but that's not really what I'm looking for. Is  
>>> there a
>>> better way to do this? Thanks for any help.
>>>
>>> params = a,b,c and r
>>> a,b,c are the fitted center point of the sphere, r is the radius
>>>
>>> err = distance-to-center - radius
>>> err = sqrt( x-a)**2 + (y-b)**2 + (z-c)**2) - r
>>>
>>>
>>>
>>> ----
>>> James J. Vincent, Ph.D.
>>> National Cancer Institute
>>> National Institutes of Health
>>> Laboratory of Molecular Biology
>>> Building 37, Room 5120
>>> 37 Convent Drive, MSC 4264
>>> Bethesda, MD 20892 USA
>>>
>>> 301-451-8755
>>> jjv5 at nih.gov
>>>
>>>
>>>
>>> _______________________________________________
>>> SciPy-user mailing list
>>> SciPy-user at scipy.org
>>> http://projects.scipy.org/mailman/listinfo/scipy-user
>>>
>>>
>>>
>
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>
>
> -- 
> David Douard                             LOGILAB, Paris (France)
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----
James J. Vincent, Ph.D.
National Cancer Institute
National Institutes of Health
Laboratory of Molecular Biology
Building 37, Room 5120
37 Convent Drive, MSC 4264
Bethesda, MD 20892 USA

301-451-8755
jjv5 at nih.gov


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