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Al Azhar University Engineering Journal, Vol. 1, No. 2, pp. 114-120, 1998
Published by Faculty of Engineering, Al Azhar University, Cairo - Egypt
A KNOWLEDGE-BASED STELLAR IMAGE INTERPRETATION SYSTEM
Mohamed A. Madkour*, A. El-Bassuny Alawy**, M. S. Ella** and Farag I. Younis**
* Systems & Computers Engineering Dept., Faculty of Engineering, Al-Azhar Univ.
** National Research Institute of Astronomy and Geophysics, Helwan.
Abstract
A knowledge based system for interpreting astronomical digital images has been developed. The system examines a given stellar
image produced by special light sensitive detectors, and employs rule-based knowledge about the light intensity profile of typical
stars in order to identify the true stars and filter out other objects. Forward chaining is used initially to scan the light intensity in a
given image searching for pixels having local maxima. Next backward chaining is employed to classify the obtained maxima into
true stars, cosmic rays, and noise peaks. The developed system is both simple and fast. It is implemented in C language and runs
on a personal computer. Obtained results are found to be in good agreement with other obtained from more complex traditional
package.
Keywords : Knowledge-based system, Stellar images, Image processing.
1. INTRODUCTION
Astronomical optical telescopes are considered the
basic instruments for observing the celestial objects. So
astronomers built new large modern telescopes or
developed their imaging systems to enhance their data
acquisition ability. The higher quantum efficiency of the
astronomical imaging system, the maximum the ability
of the system to observe very faint objects in short time
and the better the accuracy of the image obtained, even
with moderate size telescopes. The advent of Charge
Coupled Devices (CCD) imaging has spawned a
veritable revolution in astronomy. In truth, the greatest
limitation of astronomy has always been, and still is, the
fact that faint objects are being imaged [1,2].
CCD detectors are capable to record a huge number
of star images on a single frame. When the number of
stellar images in a data frame is small, the task of
identifying each image is easily performed by a human
eye and brain examining a pictorial representation of a
digital image. As the number of stellar images is
moderately increased doing this task by hand and eye
would be an atrocious waste of human effort.
Furthermore, the CCD can record data over a much
wider dynamic range of brightness than the human eye
can perceive, all of which can be exploited by an
automatic star interpretation system [3].
At any given picture element (pixel) in the CCD
image, the data number supposedly representing
detected photons come from any of several sources : (1)
detected stars, (2) undetected stars, (3) localized image
defects and cosmic ray events, and (4) diffuse sources,
including but not necessarily limited to (a) the terrestrial
night sky, (b) scattered light in the camera, and (c) some
other astronomical sources [4,5].
For greatest usefulness the star interpretation system
should have the ability to distinguish brightness
enhancements which correspond to actual stars,
however blended from the other sources cited above.
In the last two decades, sophisticated detectors for
low light level have been designed and upgraded. In
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Mohamed A. Madkour et al. : A Knowledge-Based Image Interpretation System
addition, personal computers have been introduced and
developed as well as powerful workstations. In view of
these advents, astronomers have been able to construct
various software to handle stellar image frames,
employing numerical fitting techniques based on preassumed mathematical approximations [5]. One of the
most common such packages is DAOPHOT [4,5],
which will be considered in the present work as a
reference for comparison.
This paper presents the development of an
intelligent system for stellar image interpretation, and is
structured as follows. Section 2 specifies the problem
statement and objective. In section 3, the developed
system is generally described while its implementation
details are given in section 4. A case study is selected
and used to verify the system applicability in section 5
where a comparison is presented between the obtained
results and the published ones. Section 6 deals with the
practical considerations of the present system. Some
concluding remarks concerning the present work are
given in section 7.
representing local maxima. Next, each pixel in that list
is compared to its neighbour pixels using heuristic
backward chaining rules in order to classify it into one
of the above mentioned three types. The classification
takes place on two successive stages as will be shown
next.
4. DETAILS OF IMPLEMENTATION
Fig. 1-a shows the two dimension light intensity
distribution in a star image. As shown in Fig, 1-b, the
basic star image profile can be modeled as follows[8]:
1) The central part of the star image is a nearly uniform
disk which is surrounded by a region of steeply falling
brightness. It is approximately Gaussian in form.
2) The outer part of the profile is well represented by a
power law.
3) The In-Between part of the profile is a transition
region, which can be represented by an exponential law
or any similar function.
2. PROBLEM STATEMENT AND OBJECTIVE
The main objective of the present work is to develop
a simple and fast stellar images interpretation system
(SIIS) that should be able to identify stellar identities in
CCD image frames. The major problem facing this task
is that the obtained images contain many identities,
some of which are actually due to stars, while the others
are not though their brightness distribution are almost
as that of stellar origin . The latter may be due to some
sources as stated in section 1. Hence in order to identify
a stellar image properly one must design a reliable
approach having the ability to differentiate between true
stars and other identities. The approach deemed here is
based on knowledge-based systems.
3.
GENERAL
DESCRIPTION
DEVELOPED SYSTEM
OF
THE
KB-SIIS is a knowledge based system that examines
stellar images available in industry-standard formats [6]
and employs heuristic rules to identify stellar identities
in a given frame. The system accepts a CCD image
frame as an input and examines it to identify the
locations of true stars in the given image and filter out
other noise effects. In essence, it acts as a classifier that
scans the given frame to locate spots of relatively high
light intensity and classifies each of them as either a
true star, cosmic ray, or noise peak. SIIS employs
simple forward chaining [7] rules to obtain ,through the
scanning process, a list of high intensity pixels
115
(a) Light intensity in a two dimensional image.
Central part
In-between part
Outer part
(b) Cross section model for a star image profile
Fig. 1 The star image.
Al Azahar University Engineering Journal, Vol. 1,No. 2, July 1998
Mohamed A. Madkour et al. : A Knowledge-Based Image Interpretation System
Thus, a star image is inherently a complex object.
The physical origin of these separate parts of the profile
is not clear. Theories of atmospheric seeing exist but
they do not seem to predict unique mathematical shape
of any except the small Gaussian part of the curve.
I(x-2,y-2)
I(x-2,y-1)
I(x-2,y)
I(x-2,y+1)
I(x-2,y+2)
I(x-1,y-2)
I(x-1,y-1)
I(x-1,y)
I(x-1,y+1)
I(x-1,y+2)
I(x,y-2)
I(x,y-1)
I(x,y)
I(x,y+1)
I(x,y+2)
I(x+1,y-2)
I(x+1,y-1)
I(x+1,y)
I(x+1,y+1)
I(x+1,y+2)
I(x+2,y-2)
I(x+2,y-1)
I(x+2,y)
I(x+2,y+1)
I(x+2,y+2)
(a) 5x5 window.
and there is no more use in adopting a larger size
window.
An extremely important feature used always
to discriminate between stars and cosmic rays is the
sharpness of the light intensity profile. Actually, a
cosmic ray event will be much sharper compared to a
true star. Referring to the 5x5 window of Fig. 2-a, a
metric can be assigned as the “SHARPNESS” value
which is the ratio of the light intensity at the central
pixel to the sum of intensities at the 8-neighhbouring
pixels. It represents the sharpness of the light intensity
profile. This metric will be used in the rule base, and
is given by [4]:
1 1

sharpness  I( x, y)    I( x  i, y  j)
i1 j1

1
(1)
[( i  0 and j  0 )]
(b) Directions for checking the intensity change.
Fig. 2 Schematic diagram of the pixel array intensity
distribution.
In the following heuristic rules, the first one checks
for the presence of a peak intensity pixel. Given such
pixel, the other rules check for intensity changes in all
directions as shown in Fig. 2-b.
The developed system uses the features of the
central part of the star image, to identify stars in the
CCD image frame. We code this in the knowledge base
using a set of rules that infer this brightness distribution.
The system scans the entire image, looking for a region
having brightness higher than its surrounding. If such
region was found, the system presumes the presence of
a star and the coordinates of such pixel represent the
star's center.
 Rule to find a local maximum intensity pixel
The following simple commonsense logic is used to
identify the local maxima. Let x and y represent the
coordinates of the central pixel whose number of
electrons collected are parametrized by I(x, y).
Referring to Fig. 2-a, the light intensity of the other
contiguous horizontal pixels will be denoted by I(x-1,y),
I(x+1,y),...etc.. Similar representations is followed for
other neighbouring pixels..
 Rule to check the intensity decrease along the
An important star feature can be determined upon
examining the characteristics of image brightness
distribution shown in Fig. 1-a. That is the center of the
distribution has the peak value and the values decrease
outward in all directions. In the present work, we
consider a 5x5 window with the brightest pixel in its
center as shown in Fig. 2-a. The following inference
rules are designed to check the above-mentioned star
feature. It should be noted that a 5x5 window is
sufficient to show the direction of change in brightness,
IF I( x, y ) > I( x-1, y-1 )
AND I( x, y ) > I( x-1, y )
AND I( x, y ) > I( x-1, y+1 )
AND I( x, y ) > I( x, y-1 )
AND I( x, y ) > I( x, y+1 )
AND I( x, y ) > I( x+1, y-1 )
AND I( x, y ) > I( x+1, y )
AND I( x, y ) > I( x+1, y+1 )
THEN Central pixel is a local maximum
U-direction
IF Central pixel is a local maximum
AND I( x, y-1 ) > I( x-1, y-2 )
AND I( x, y-1 ) > I( x , y-2 )
AND I( x, y-1 ) > I( x+1, y-2 )
AND I( x, y-1 ) > I( x-1, y-1 )
AND I( x, y-1 ) > I( x+1, y-1 )
THEN The intensity decreases in the U direction.
Similarly, there are three other rules to check the
intensity decrease in the L, R, and D directions.
 Rule to check the intensity decrease along the
UL-direction.
IF Central pixel is a local maximum
AND I( x-1, y-1 ) > I( x-2, y-2 )
AND I( x-1, y-1 ) > I( x-1, y-2 )
AND I( x-1, y-1 ) > I( x , y-2 )
AND I( x-1, y-1 ) > I( x-2, y-1 )
Al Azahar University Engineering Journal, Vol. 1,No. 2, July 1998
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Mohamed A. Madkour et al. : A Knowledge-Based Image Interpretation System
AND I( x-1, y-1 ) > I( x-2, y )
THEN The intensity decreases in the UL direction.
Again, there are three similar rules to check the
intensity decrease in the UR, DL, and DR directions.
 True star rule
IF Central pixel is a local maximum.
AND The intensity decreased in the U direction.
AND The intensity decreased in the L direction.
AND The intensity decreased in the R direction.
AND The intensity decreased in the D direction.
AND The intensity decreased in the UL direction.
AND The intensity decreased in the UR direction.
AND The intensity decreased in the DL direction.
AND The intensity decreased in the DR direction.
THEN TRUE STAR is centered at the pixel (x,y)
Fig. 3 shows the phases used in the developed SIIS
system to identify and classify the different entities in a
CCD frame. The system scans the entire frame pixel by
pixel using forward chaining inference technique,
looking for regions whose brightness distribution obeys
the conditions stated in either case 1 or case 2. If such
regions was found, the system produces two lists.
The last rule scans the working memory looking for
matches to its premises, if matches are found, then a
TrueStar centered at pixel (x,y) is assigned. By applying
the stated rules to several stars in several standard CCD
astronomical images, the following two cases have been
observed :
Case 1 : The central part of some stars satisfies all
previous rules. Hence, stellar images can be directly
identified without ambiguity.
Case 2 : The central part of some stars partially satisfies
the previous rules. Such situation can be due to: a) low
signal to noise ratio for faint stars, b)the light of blended
stars affect each other, c) improper preprocessing
techniques for CCD image frame which causes the
central part of some stars to deviate from the previous
rules. However, a bright pixel can be classified initially
as a MayBeStar if it satisfies either one of the following
conditions.
Referring to Fig. 2-b, the light intensity should :
a) decrease in at least two directions from the {U, L, D,
and R} set, , and at least in two directions from the
{UL, UR, DL, and DR} set., or
b) decrease in at least three directions from the {U, L,
D, R} set.
Consequently, further processing should be carried
out to determine the nature of a MayBeStar pixel. For
example, the following rule is used to check for the
conditions of case (2-a).
MayBeStar centered at the pixel (x,y)
IF
The intensity decreases in the U direction, AND
The intensity decreases in the L direction, AND
The intensity decreases in the UL direction, AND
The intensity decreases in the UR direction.
117
Similar rules can be developed based on the
conditions stated in case 2 above. However, upon
applying such rules, the resulting set of MayBeStar
pixels would include other stellar identities in addition
to true stars. Namely, these are cosmic rays and noise
peaks.
i) A TrueStar list for all pixels that fulfill the
conditions of case 1.
ii) A MayBeStar list for all pixels obeying case 2.
For each identity in both lists, the following
information are recorded :
 the coordinates of the brightest pixel at the center
of the region.
 the intensity of that pixel.
 the sharpness value computed by equation 1.
Start
Read image frame
Forward chaining process to identify the pixels
having local maxima (of light intensity) and
classify them as either TrueStar or MayBeStar
Determine minimum intensity and
maximum sharp from the TrueStar list
Backward chaining process to examine
the MayBeStar list
in order to classify its contents into :
TRUE STAR, COSMIC RAY, and NOISE
Stop
Fig. 3 Classification phases in the KB-SIIS system.
Al Azahar University Engineering Journal, Vol. 1,No. 2, July 1998
Mohamed A. Madkour et al. : A Knowledge-Based Image Interpretation System
Through these phases two parameters are evaluated
from the TrueStar list which are:
 Threshold value : which is the minimum
intensity value.
 Maximum Sharpness : which is the maximum
sharpness value.
CCD frames, some other useful applications were
found. The developed new approach provides an
efficient and quick tool to evaluate the quality of
individual CCD images as well as the whole frame. This
can be evident through the following aspects.
The KB-SIIS system automatically computes these
parameters for each entry in the MayBeStar list to allow
objective classification without user intervention.
However, a user interaction mode is provided in the
SIIS system in order to allow an experienced user to
enter a better threshold value which may lead to find out
more true stars.
Three backward chaining rules are then used to
classify the entities of the MayBeStar list as truestar,
cosmic ray, or noise peak. These rules are :
A MayBeStar centered at pixel(x,y) is a TRUE STAR IF
The intensity at (x,y)  Threshold value, AND
The sharpness at (x,y)  Maximum Sharpness.
A MayBeStar centered at pixel(x,y) is a COSMIC RAY IF
The intensity of that pixel  Threshold value, AND
The sharpness at (x,y) > Maximum Sharpness.
A MayBeStar centered at pixel(x,y) is NOISE IF
The intensity of that pixel < Threshold value.
5. CASE STUDY
Fig. 4 shows a picture of the test case which is a
CCD image of the star cluster M67 [9]. It is
characterized by:
 Size of 320 x350 pixels,
 Optical filter used is visual filter,
 Exposure time =30 Sec.
 Maximum pixel value = 16252 ADU (Analog
/Digital converter unit),
 Minimum pixel value = 9 ADU.
Upon analysing this image using the DAOPHOT
package, 134 stars have been reported via the user
interaction mode where the threshold value is provided
by the user. On the other hand, using the same threshold
value, the developed KB-SIIS system has identified 131
stars, two of which are not identified by DAOPHOT.
This shows that the results obtained by both systems are
in a very good agreement, which is clearly depicted in
Fig. 5. Detailed astronomical discussion and
interpretation are being [10].
6- PRACTICAL CONSIDERATIONS
While our main purpose is primarily concerned to
propose a KB-system for identifying stellar images on
Fig. 4 CCD image of the star cluster M67
 Adopting the proper exposure time (ET) :
After obtaining a trial frame in an observing night,
the SIIS system can be used to find pixels having
maximum
and
minimum
electron
number,
corresponding to the brightest and faintest stars
observed respectively. If the Max value found (or that of
the target object) is less than the Full Well Capacity
(FWC) of the chip used, then it is advisable to increase
the ET to enhance signal-to-noise (S/N) ratio. On the
other hand, when some contiguous pixels (or one pixel)
were found to have electron number close ,or equal to
the FWC, one concludes that the ET used is too long
and has to be shortened. Since the SIIS system takes
few seconds for a medium size frame it serves an as on
line tool to adopt the proper ET for the whole frame or
stars of interest.
 Check the optical alignment:
It is necessary to set the CCD chip surface
perpendicular to the telescope principal optical axis.
Any deviation (tilt or shift) from such position will
produce elongated images for stars being observed.
When snapping an over-exposed frame for a field of
Al Azahar University Engineering Journal, Vol. 1,No. 2, July 1998
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Mohamed A. Madkour et al. : A Knowledge-Based Image Interpretation System
crowded and bright stars and examining images in
different areas (one near the centre and one near each
corner), it is possible to judge whether the optical
alignment is correct. For an over exposed frame and
proper alignment the saturated areas in each image
should be square in shape. If this is not afforded the
camera and/or the telescope mirrors can be adjusted to
produce such condition.
 Best focus determination:
The present approach can help effectively in setting
the camera at the telescope focus in short time. This can
be achieved by obtaining some frames where the camera
at different positions along the optical axis. Images of
some stars are examined to assign the frame whose
images have smallest number of pixels. The camera
position where such frame is obtained corresponds the
proper position of the best telescope focus.
350
50
100
150
200
250
300
350
300
250
250
200
200
150
150
100
100
Y
300
50
50
necessary telescope movements to be done to redirect it
to the first direction.
7. CONCLUSION
In the present work, a knowledge-based system is
developed to interpret stellar images obtained by a CCD
camera. Based on well known star features, heuristic
rules are presented to show how to identify true stars in
a given image and filter out other similar entities such
as cosmic ray events and noise peaks. In spite of its
simplicity, the system achieves good accuracy
compared with the results of more complex
conventional programs. The advantages of the
developed system are summarized in the following :
i- Ease of expansion
The expert system separates the knowledge
contained in the knowledge base from its control
performed by the inference engine. This feature permits
one to easily modify the rules allowing for a graceful
expansion of the system’s knowledge.
ii- Simplicity
Conventional package such as DAOPHOT are based
on the stellar image profile fitting technique, which
repeats a complex algorithm at each pixel of the image
to determine whether or not it is a star. On the other
hand, the developed expert system uses forward
chaining to reduce the search space based on simple
reasoning rules. Consequently, its complexity is much
less than conventional programs since it needs only to
examine a typically small subset of the image pixels to
find out the stars.
iii- Speed of execution
50
100
150
200
250
300
X
Fig. 5
Stars identified by both DAOPHOT program
(o) and the developed SIIS system () .
 Telescope guiding appraisal:
In future, SIIS can be extended in various directions:
Any error in the telescope guiding or changes in
atmospheric refraction during acquiring a CCD frame
produces distortion in the uniformity of stellar images.
This can be easily detected through the SIIS rules.
Hence the guiding process can be assessed and the
quality of the images is evaluated. As a by product of
using SIIS, it is feasible to use this system for
autoguiding the telescope. Applying it on a star image
in a small area in the frame provides ,instantaneously,
the peak position which can be compared with that in
the next frames. Any change in position can be detected
and interpreted ,through certain electronic devices, as
119
Compared with available conventional programs, the
SIIS can achieve almost the same accuracy in much less
time. This property (viz. high speed) has some useful
practical implications as given in section 6.
First, to improve the accuracy of star identification :
a- The system can be augmented with an artificial
neural network (ANN) classifier trained to recognize the
features of true stars. This is the subject of a companion
paper.
b- The user interaction mode can be automated by
developing an extra expert system to capture the
expertise of experienced users and automatically set the
threshold value to the best possible value.
Al Azahar University Engineering Journal, Vol. 1,No. 2, July 1998
Mohamed A. Madkour et al. : A Knowledge-Based Image Interpretation System
Second, the capabilities of the system can be
extended to identify galaxies that can be found in the
image.
Third, the system can be supplemented to derive
photometric information for the stars.
5. Peter B. Stetson, 1992, “Further progress in
CCD photometry”, Proceeding of IAU
Colloquium Stellar Photometry , 136.
6. http:// fits.gsfc.nasa.gov/fits_home.html, 1997.
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7. John Durkin,1994, “Expert Systems Design and
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Al Azahar University Engineering Journal, Vol. 1,No. 2, July 1998
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