This thesis is focused on the automatic detection of sources (objects) in astronomical images. These sources are something that suggests the presence of an astronomical object such as stars, galaxies, gas, dust or cosmic rays. The use of automatic algorithms to perform such a task becomes of great importance in the astronomical field because of the increasing amount of data and the inefficiency and inaccuracy of manual inspection.<\/p>\n
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In the first place, we exhaustively analyze the state of the art on this topic, presenting a new classification of techniques and pointing out their main strengths and weaknesses. A complementary quantitative evaluation of some of the most remarkable methods that appeared in the literature over the last few years is also provided.<\/p>\n
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Afterwards, we present three different proposals to detect faint sources (those with brightnesses so weak that are almost imperceptible by the naked eye) in radio images: the first, a method that combines a multiscale decomposition and local thresholding (WALT); the second, a method based on the structural behaviour of the intensities of sets of adjacent pixels (RCF); and the third, a supervised method that classifies pixels by means of local features (filtered patches).<\/p>\n
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Finally, we also present a new proposal to deal with infrared and radio images. This method, called multiscale distilled sensing (MDS), is based on the combined use of a multiscale transform and an innovative method called distilled sensing which iteratively discards background (sky) pixels.<\/p>\n
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The experimental results and the evaluation performed with synthetic and real data points out that the performances of our four proposals are better than state-of-the-art approaches in terms of both reliability and completeness of the detections provided. The following figure depicts a sample of the qualitative results achieved by the MDS method. The contrast between the great number of sources correctly detected (encircled in green) and the few number of spurious detections (encircled in red) can be clearly noted.<\/p>\n
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Aquesta tesi se centra en la detecci\u00f3 autom\u00e0tica de fonts (objectes) en imatges astron\u00f2miques. Aquestes fonts s\u00f3n l’origen de quelcom que suggereix la pres\u00e8ncia d’un objecte astron\u00f2mic, com ara estrelles, gal\u00e0xies, gas, pols o rajos c\u00f2smics. L’\u00fas d’eines autom\u00e0tiques per a realitzar aquest tipus de tasca esdev\u00e9 d’una gran import\u00e0ncia en l’\u00e0mbit astron\u00f2mic degut a la creixent quantitat de dades i a la inefici\u00e8ncia i imprecisi\u00f3 de les inspeccions manuals.<\/p>\n
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En primer lloc, s’analitza de forma exhaustiva l’estat de l’art d’aquest tema, presentant una nova classificaci\u00f3 de t\u00e8cniques i destacant els seus principals punts forts i febles. Complement\u00e0riament, tamb\u00e9 es proporciona una avaluaci\u00f3 quantitativa d’alguns dels m\u00e8todes m\u00e9s destacats que han aparegut en els \u00faltims anys.<\/p>\n
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En segon lloc, es presenten tres propostes diferents per a la detecci\u00f3 de fonts febles (aquelles amb brillantors tan febles que f\u00e0cilment passen desapercebudes a simple vista) en imatges de radiofreq\u00fc\u00e8ncia: la primera, anomenada WALT, descompon la imatge en diverses escales i les binaritza localment; la segona, anomenada RCF, es basa en el comportament estructural de grups de p\u00edxels adjacents; i la tercera, realitza una classificaci\u00f3 supervisada de p\u00edxels per mitj\u00e0 de caracter\u00edstiques locals.<\/p>\n
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Finalment, tamb\u00e9 es presenta una nova proposta per tractar amb imatges infraroges i de radiofreq\u00fc\u00e8ncia. Aquest m\u00e8tode s’anomena multiscale distilled sensing (MDS) ja que es basa en l’\u00fas combinat d’una t\u00e8cnica multiescala i un m\u00e8tode molt innovador que descarta iterativament p\u00edxels del fons (cel) anomenat distilled sensing.<\/p>\n
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Els resultats experimentals i l’avaluaci\u00f3 duta a terme amb imatges sint\u00e8tiques i reals han demostrat que el rendiment de les quatre propostes \u00e9s millor que el d’altres m\u00e8todes de l’estat de l’art, tant pel que fa a fiabilitat com a completesa. La seg\u00fcent figura mostra els resultats qualitatius obtinguts amb el m\u00e8tode MDS en una imatge infraroja. Clarament s’hi pot apreciar el contrast entre la gran quantitat de fonts detectades correctament (en verd) i les poques deteccions incorrectes (en vermell).<\/p>\n","protected":false},"excerpt":{"rendered":"