Astronomical images provide information about the great variety of celestial objects in the Universe, the physical processes taking place in it, and the formation and evolution of the cosmos. Great efforts are made to automatically detect stellar bodies in images due to the large volumes of data and the fact that the intensity of many sources is at the detection level of the instrument. In this paper, we review the main approaches to automated source detection. The main features of the detection algorithms are analysed and the most important techniques are classified into different strategies according to their type of image transformation and their main detection principle; at the same time their strengths and weaknesses are highlighted. A qualitative and quantitative evaluation of the results of the most representative approaches is also presentedThis work has been supported by Grant AYA2010-21782-C03-02 from EMCI-Ministerio de Ciencia e Innovacion. MM holds an FI grant 2011FI_B 0008

A variety of software is used to solve the challenging task of detecting astronomical sources in wide field images. Additionally, computer vision methods based on well-known or innovative techniques are arising to face this purpose. In this paper, we review several of the most promising methods that have emerged during the last few years in the field of source detection. We specifically focus on methods that have been designed to deal with images with Gaussian noise distributions. The singularity of this analysis is that the different methods have been applied to a single dataset consisting of optical, infrared, and radio images. Thus, the different approaches are applied on a level playing field, and the results obtained can be used to evaluate and compare the methods in a meaningful, quantitative way. Moreover, we present the most important strengths and weaknesses of the methods for each type of image as well as an extensive discussion where the methods with best performances are highlightedThis work has been supported by Grant AYA2010-21782-C03-02 from EMCI - Ministerio de Ciencia e Innovacion. M. Masias holds an FI grant 2012FI_B1 00122. This research has used data from SDSS-III. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III web site is http://www.sdss3.org/. This publication makes use of data products from the Wide-field Infrared Survey Explorer, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration. This research has used the facilities of the Canadian Astronomy Data Centre operated by the National Research Council of Canada with the support of the Canadian Space Agency. The research presented in this paper has used data from the Canadian Galactic Plane Survey, a Canadian project with international partners supported by the Natural Sciences and Engineering Research Counci

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