<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-17T07:01:07Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:10256/11702" metadataPrefix="marc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:10256/11702</identifier><datestamp>2024-05-22T09:48:50Z</datestamp><setSpec>com_2072_452955</setSpec><setSpec>com_2072_2054</setSpec><setSpec>col_2072_452957</setSpec></header><metadata><record xmlns="http://www.loc.gov/MARC21/slim" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
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      <subfield code="a">Masias Moyset, Marc</subfield>
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      <subfield code="a">Peracaula i Bosch, Marta</subfield>
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      <subfield code="a">Freixenet i Bosch, Jordi</subfield>
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      <subfield code="a">Lladó Bardera, Xavier</subfield>
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      <subfield code="a">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 highlighted</subfield>
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      <subfield code="a">This 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 Council</subfield>
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      <subfield code="a">A quantitative analysis of source detection approaches in optical, infrared, and radio astronomical images</subfield>
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