Segmenting extended structures in radio astronomical images by filtering bright compact sources and using wavelets decomposition

Peracaula, M.; Oliver, Arnau; Torrent, Albert; Lladó, Xavier; Freixenet, Jordi; Martı́, Joan

doi:10.1109/icip.2011.6116254

Cited by 8 publications

(13 citation statements)

References 6 publications

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“…In other cases, such as when there is interest in highlighting extended sources, the lower wavelet scales are more suitable (see for instance Peracaula et al, 2011).…”

Section: Algorithm Stepsmentioning

confidence: 98%

Exploring three faint source detections methods for aperture synthesis radio images

et al. 2015

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“…In other cases, such as when there is interest in highlighting extended sources, the lower wavelet scales are more suitable (see for instance Peracaula et al, 2011).…”

Section: Algorithm Stepsmentioning

confidence: 98%

Exploring three faint source detections methods for aperture synthesis radio images

et al. 2015

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“…Modelling the intensity of the image pixels as a Gaussian, the bell‐shaped zone may be considered as noise, while the rest of the distribution may represent background and objects. This noise filtering by Gaussian fitting of the histogram was used by Slezak et al (1988), and more recently by Peracaula et al (2009b, 2011). Fig.…”

Section: Image Transformationmentioning

confidence: 99%

“…They computed the histogram of pixel intensities at each window, and set the threshold at 1.5 times the deviation distribution. Other approaches have recently been proposed by Peracaula et al (2009b, 2011) and Torrent et al (2010), who defined the local threshold by means of the local noise determined by the pixel intensity histogram, or by Melin et al (2006), who used a multiple value of the SNR. Yang et al (2008) used a method to automate threshold calculation called the Otsu method (Otsu 1979), where the intra‐class variance is minimized to get a good threshold.…”

Section: Detection Criteriamentioning

confidence: 99%

A review of source detection approaches in astronomical images

Masias

Freixenet

Lladó

et al. 2012

Monthly Notices of the Royal Astronomical Society

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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

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“…A simplest way to obtain this translational invariance is not to perform any sub-sampling [9][10][11]. The filters are to be up-sampled at each level of decomposition by padding low and high pass filters with zeros [10][11][12]. This method is commonly referred to as the "Ã Trous" algorithm [11,12].…”

Section: Stationary Wavelet Transformmentioning

confidence: 99%

“…The presence of point sources can adversely affect the evolution of the level set contour which is taken care by eroding the bright sources. Total Variation Denoising [13,14] applied to the Stationary Wavelet Decomposed image [11,12] satisfactorily removes noise with edge preservation. Adaptive histogram equalization technique is used to enhance the contrast of the image.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Algorithm for the Segmentation of Astronomical Images

Xavier¹

2012

IOSRJCE

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In this paper, an efficient algorithm for segmenting celestial objects from astronomical images is proposed. The proper segmentation of astronomical objects like planets, comets, galaxies, asteroids etc. is a difficult task due to the presence of innumerous bright point sources in the frame, presence of noise, weak edges of celestial objects, low contrast etc. In order to overcome these bottlenecks, multiple preprocessing steps are performed on the actual image prior to segmenting the desired object(s). Level Set segmentation is the key technique of this proposed method. The result of the proposed algorithm on various celestial objects substantiates the effectiveness of the proposed method.

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Segmenting extended structures in radio astronomical images by filtering bright compact sources and using wavelets decomposition

Cited by 8 publications

References 6 publications

Exploring three faint source detections methods for aperture synthesis radio images

Exploring three faint source detections methods for aperture synthesis radio images

A review of source detection approaches in astronomical images

An Efficient Algorithm for the Segmentation of Astronomical Images

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