Object signal detection is the basic step by which astrophysicists can retrieve information about celestial bodies. In astrophysics, most of the information about celestial bodies are obtained from astronomical images. A major aim of scientific research carried out with astronomical images is to generate catalogs of objects. This leads to, for example, studies of number counts, clustering properties, or colour/magnitude distributions. Obviously, in all cases the reliability of the sources extraction process is of great importance. As Charge-Coupled Devices (CCDs) become the new generation of astronomical detectors, they are used widely in astronomical research. Because of the digital nature of the CCD images, people can use computer techniques to process and then extract useful information. Many methods have been constructed to detect object signals from digitized images. Important examples are DOPHOT ,DAOPHOT/ALLFRAME , FOCAS , SExtractor , IRAF and COSMOS packages,which can measure locations, magnitudes and types of astronomical objects from digitized images. Although these methods are powerful and are commonly used in research,they also have some disadvantages. Their detection steps assume simple background model, so in images with backgrounds of complex variations these methods can miss some low S/N signals that are still statistically significant. Degrees of automation of some methods are low so users need a lot of manual intervention, which becomes a main obstacle in survey projects. I and my supervisorintroduce a new algorithm to improve these handling problems. One application of the new algorithm is to detect atomic emission/absorption lines in spectral data and obtain the redshifts of astronomical objects. To automatically measure redshifts of a huge amount of galaxy spectra is a challenge in survey projects. For example the LAMOST telescope aims to obtain 107galaxies spectra, which must be processed automatically. Our new algorithm can be applied to such survey projects and is expected to find extensive applications. In Sect.1 the CCD and methods of digital image processing are briefly introduced. In order to let readers better understand image processing, Sect.2 introduces an example of object detection/measurement in digital iamges—CCD stellar photometry. Sect.3 describes our new algorithm in detail. We apply our new algorithm to the GAMA galaxy spectral survey to detect atomic lines and calculate galaxies redshfits in Sect.4. In sect.5 we discuss future applications of our new algorithm.
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