[Numpy-discussion] Finding Star Images on a Photo (Video chip) Plate?
Johann Cohen-Tanugi
cohen at lpta.in2p3.fr
Sat May 29 17:04:57 EDT 2010
I have used sextractor in the past :
http://terapix.iap.fr/rubrique.php?id_rubrique=91/
Johann
On 05/29/2010 08:29 PM, Wayne Watson wrote:
> I managed to find some FORTRAN code that is relevant. Algorithms for CCD
> Stellar Photometry,K. Mighell, Kitt Peak,
> <http://www.adass.org/adass/proceedings/adass98/mighellkj/>. He
> discusses PSF and provides a few "starter" programs, centroid and peak
> determination. He discusses sky background (, correction, cleanup?) and
> points to his photometry package).
>
> My chip is not big at all, about 1/2". No large chips for what I'm
> doing. I don't mind starting with simple methods to see how far they
> get me. That's a learning experience that often provides a better
> understanding of the whole process. At this point, this is about
> exploration to see how setting up an image to analyze can be
> accomplished. Once done, then well known methods are available for my
> purpose.
>
> Where this is going at the moment is to do source extraction. Towards
> that end I found an interesting source that does a good job of
> summarizing how one does it. Here's an excerpt.
> =====================================
> Star-Extraction Algorithms
> The conventional star-extraction algorithms[4,5] widely used for
> ground-based data reductions are comprised of two basic steps. The first
> step is to fit the sky background in order to detect the star pixels
> from the background. The second is to search for pixels that belong to
> the same star, and group them together to compute the center coordinates
> of the star. A typical implementation of such an algorithm is described
> as below.
> 1)
> Background Estimation
> First, the sky background in each pixel is estimated in order to
> seperate the object signal from the background signal[4]. The image is
> divided into many grid cells, each including a number of pixels. In each
> grid cell, an estimate of the background level is made and is assigned
> to the center pixel as its nominal value. A relatively "coarse" mesh of
> the sky background distribution is thus constructed. The value at any
> other pixel can be obtained from 2-dimensional spline-fitting
> interpolation of the mesh.
> 2)
> Searching for Star Pixels
> After the interpolated sky background is subtracted from the image, the
> sky contribution to any star pixel becomes negligible. Only small
> residues remain in the sky pixels and the mean residue value can be
> taken as the standard deviation of the background. After convolution
> with a Gaussian filter, the image is traversed to search for pixels
> whose values are N times higher than the background standard deviation.
> Each group of connected high-value pixels are joined together to form an
> extracted star.
> The conventional algorithms described above are time-consuming, although
> they have the advantage of detecting sources with very low
> signal-to-noise ratios (SNRs). They are not suitable for satellite
> onboard data reductions since the onboard computers are usually several
> hundred times slower than even a normal personal computer available on
> the ground.
>
> SciPy. Hmm, this looks reasonable.<http://scipy.org/Getting_Started>.
> Somehow I missed it and instead landed on
> <http://www.scipy.org/wikis/topical_software/Tutorial>, which looks
> fairly imposing. Those are tar and gzip files. In any case, the whole
> page looks quite imposing. Where did you find the simple one below?
> ===============================
>
>
> On 5/28/2010 8:31 PM, Anne Archibald wrote:
>
>> On 28 May 2010 23:59, Wayne Watson<sierra_mtnview at sbcglobal.net> wrote:
>>
>>
>>> That opened a few avenues. After reading this, I went on a merry search with
>>> Google. I hit upon one interesting book, Handbook of CCD astronomy (Steve B.
>>> Howell), that discusses PSFs. A Amazon Look Inside suggests this is mostly
>>> about h/w. I tried to figure out how to reach the scipy mail list, but, as
>>> once a year ago, couldn't figure out the newsgroup GMANE connection. This
>>> search recalled to mind my Handbook of Astro Image Processing by Berry and
>>> Burnell. It has a few pages on the PSF. In the ref section for that
>>> material(PSFs) there's another ref to Steve Howell that may be of use: Astro
>>> CCD Observing and Reduction Techniques, ASP, Pacific Conf. Series, vol. 23,
>>> 1992. There are further Berry and Burnell refs that may be applicable.
>>>
>>>
>> Ah, sorry, I've been at an astro conference all week, I should have
>> expanded that acronym. PSF is short for "Point Spread Function"; the
>> idea is that with an optically good telescope, a point source anywhere
>> in the field of view produces a blob of characteristic shape (often
>> roughly a two-dimensional Gaussian) in your detector. The shape and
>> size of this blob is set by your optics (including diffraction) and
>> the atmospheric seeing. A star, being intrinsically a point source,
>> produces a brighter or less bright version of this blob centered on
>> the star's true position. To accurately measure the star's position
>> (and often brightness) one usually fits a model blob to the noisy blob
>> coming from the star of interest.
>>
>> I should note that this requires you to have more pixels than you
>> "need", so that even a point source is spread over many pixels;
>> without this it's impossible to get subpixel positioning (among other
>> things). Older consumer digital cameras often lacked this, since it
>> was difficult to put enough pixels on a CCD, but fortunately megapixel
>> mania has helpfully ensured that no matter how sharp the focus, every
>> feature in your image is smeared over many pixels.
>>
>>
>>
>>> I probed IRAF, SciPy, and Python, but it looks like a steep learning curve.
>>> The SciPy tutorial page looks like overkill. They have what looks like very
>>> large tutorials. Perhaps daunting. I did a quick shot at pyraf, a tutorial
>>> page, but note it has a prereq of IRAF. Another daunting path.
>>>
>>>
>> Wait, you think SciPy has too many tutorials? Or that they're too
>> detailed? Just pick a short, easy, or sketchy one then. Here's one
>> that's all three:
>>
>>
>>
>>>>> import scipy.stats
>>>>> scipy.stats.norm.cdf(3)
>>>>>
>>>>>
>> 0.9986501019683699
>>
>> That's the value of the CDF of a standard normal at three sigma, i.e.,
>> one minus the false positive probability for a one-sided three sigma
>> detection.
>>
>>
>>
>>> Well, maybe a DIY approach will do the trick for me.
>>>
>>>
>> I haven't used IRAF yet (though I have data sets waiting), and I do
>> understand the urge to write your own code rather than understanding
>> someone else's, but let me point out that reliably extracting source
>> parameters from astronomical images is *hard* and requires cleverness,
>> attention to countless special cases, troubleshooting, and experience.
>> But it's an old problem, and astronomers have taken all of the needed
>> things listed above and built them into IRAF. Do consider using it.
>>
>> Anne
>>
>>
>>
>>> On 5/28/2010 5:41 PM, Anne Archibald wrote:
>>>
>>> On 28 May 2010 21:09, Charles R Harris<charlesr.harris at gmail.com> wrote:
>>>
>>>
>>> On Fri, May 28, 2010 at 5:45 PM, Wayne Watson<sierra_mtnview at sbcglobal.net>
>>> wrote:
>>>
>>>
>>> Suppose I have a 640x480 pixel video chip and would like to find star
>>> images on it, possible planets and the moon. A possibility of noise
>>> exits, or bright pixels. Is there a known method for finding the
>>> centroids of these astro objects?
>>>
>>>
>>>
>>> You can threshold the image and then cluster the pixels in objects. I've
>>> done this on occasion using my own software, but I think there might be
>>> something in scipy/ndimage that does the same. Someone here will know.
>>>
>>>
>>> There are sort of two passes here - the first is to find all the
>>> stars, and the second is to fine down their positions, ideally to less
>>> than a pixel. For the former, thresholding and clumping is probably
>>> the way to go.
>>>
>>> For the latter I think a standard approach is PSF fitting - that is,
>>> you fit (say) a two-dimensional Gaussian to the pixels near your star.
>>> You'll fit for at least central (subpixel) position, probably radius,
>>> and maybe eccentricity and orientation. You might even fit for a more
>>> sophisticated PSF (doughnuts are natural for Schmidt-Cassegrain
>>> telescopes, or the diffraction pattern of your spider). Any spot whose
>>> best-fit PSF is just one pixel wide is noise or a cosmic ray hit or a
>>> hotpixel; any spot whose best-fit PSF is huge is a detector splodge or
>>> a planet or galaxy.
>>>
>>> All this assumes that your CCD has more resolution than your optics;
>>> if this is not the case you're more or less stuck, since a star is
>>> then just a bright pixel. In this case your problem is one of
>>> combining multiple offset images, dark skies, and dome flats to try to
>>> distinguish detector crud and cosmic ray hits from actual stars. It
>>> can be done, but it will be a colossal pain if your pointing accuracy
>>> is not subpixel (which it probably won't be).
>>>
>>> In any case, my optical friends tell me that the Right Way to do all
>>> this is to use all the code built into IRAF (or its python wrapper,
>>> pyraf) that does all this difficult work for you.
>>>
>>> Anne
>>> P.S. if your images have been fed through JPEG or some other lossy
>>> compression the process will become an utter nightmare. -A
>>>
>>>
>>>
>>> Chuck
>>>
>>>
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>>>
>>>
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>>>
>>>
>>> --
>>> Wayne Watson (Watson Adventures, Prop., Nevada City, CA)
>>>
>>> (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time)
>>> Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet
>>>
>>> There are no statues or memorials dedicated to
>>> Thomas Paine for his substantial part in the
>>> American Revolution.
>>>
>>> -- An observation in The Science of Liberty
>>> by Timothy Ferris
>>>
>>>
>>> Web Page:<www.speckledwithstars.net/>
>>>
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>>>
>>>
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