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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 114 ISSN 1110 – 6409 1998 AUEJ All rights reserved 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) i1 j1 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 116 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 118 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. REFERENCES 1. Ian S. Mclean, 1997, “Electronic Imaging In Astronomy, Jon Wiley & Sons Ltd. 2. Christian Buil, 1991, Willmann-Bell, Inc. Program for Crowded-field Stellar Photometry”, Publications of the Astronomical Society of the Pacific, 99, 191-222. “CCD Astronomy”, 3. Christopher D.,1993, “ Modern processing”, Academic Press Inc., USA. Image 4. Peter B. Stetson , 1987, “DAOPHOT : A Computer 7. John Durkin,1994, “Expert Systems Design and Development”, Macmillan pub. Comp. 8. King I. R., 1971, “The profile of a star image”, Publications of the Astronomical Society of the Pacific, 83, 199. 9. http://david.fiz.uni-lj.si/astro/daophot.html, 1996. 10. F. I. Younis, Knowledge-Based Image Interpretation Systems : An Astronomical Application, M. Sc.Thesis, AlAzhar Univ.,1998. Al Azahar University Engineering Journal, Vol. 1,No. 2, July 1998 120