We present exploratory work into the application of the topic modelling algorithm latent Dirichlet allocation (LDA) to image segmentation in greyscale images, and in particular, source detection in radio astronomy images. LDA performed similarly to the standard source-detection software on a representative sample of radio astronomy im-ages. Our use of LDA underperforms on fainter and diffuse sources, but yields superior results on a representative im-age polluted with artefacts — the type of image in which the standard source-detection software requires manual in-tervention by an astronomer for adequate results
We apply a Machine Learning technique known as Convolutional Denoising Autoencoder to denoise synthe...
Latent Dirichlet Allocation (LDA) is a scheme which may be used to estimate topics and their probabi...
Zhang, Fadili, & Starck have recently developed a denoising procedure for Poisson data that offers a...
The sheer volume of data to be produced by the next generation of radio telescopes—exabytes of data ...
Abstract. The next generation of radio telescopes will generate exabytes of data on hundreds of mill...
This paper describes research that seeks to supersede human inductive learning and reasoning in high...
Following the trend of “segmentation for recognition”, we present 2LDA, a novel generative model to ...
Two new extensions of latent Dirichlet allocation (LDA), denoted topic-supervised LDA (ts-LDA) and c...
A variety of software is used to solve the challenging task of detecting astronomical sources in wid...
The high sensitivities of modern radio telescopes will enable the detection of very faint astrophysi...
Automated source extraction and parametrization represents a crucial challenge for the next-generati...
Aims. Image formation for radio astronomy can be defined as estimating the spatial intensity distrib...
International audienceZhang, Fadili, & Starck have recently developed a denoising procedure for Pois...
In this paper we introduce a reliable, fully automated and fast algorithm to detect extended extraga...
Fundamental scientific questions such as how the first stars were formed or how the universe came in...
We apply a Machine Learning technique known as Convolutional Denoising Autoencoder to denoise synthe...
Latent Dirichlet Allocation (LDA) is a scheme which may be used to estimate topics and their probabi...
Zhang, Fadili, & Starck have recently developed a denoising procedure for Poisson data that offers a...
The sheer volume of data to be produced by the next generation of radio telescopes—exabytes of data ...
Abstract. The next generation of radio telescopes will generate exabytes of data on hundreds of mill...
This paper describes research that seeks to supersede human inductive learning and reasoning in high...
Following the trend of “segmentation for recognition”, we present 2LDA, a novel generative model to ...
Two new extensions of latent Dirichlet allocation (LDA), denoted topic-supervised LDA (ts-LDA) and c...
A variety of software is used to solve the challenging task of detecting astronomical sources in wid...
The high sensitivities of modern radio telescopes will enable the detection of very faint astrophysi...
Automated source extraction and parametrization represents a crucial challenge for the next-generati...
Aims. Image formation for radio astronomy can be defined as estimating the spatial intensity distrib...
International audienceZhang, Fadili, & Starck have recently developed a denoising procedure for Pois...
In this paper we introduce a reliable, fully automated and fast algorithm to detect extended extraga...
Fundamental scientific questions such as how the first stars were formed or how the universe came in...
We apply a Machine Learning technique known as Convolutional Denoising Autoencoder to denoise synthe...
Latent Dirichlet Allocation (LDA) is a scheme which may be used to estimate topics and their probabi...
Zhang, Fadili, & Starck have recently developed a denoising procedure for Poisson data that offers a...