Related papers: A Semi-Automated Computational Approach for Infrar…
Convolutional Neural Networks (CNNs) constitute a class of Deep Learning models which have been used in the recent past to resolve many problems in computer vision, in particular optical flow estimation. Measuring displacement and strain…
Infrared Dark Clouds appear to be the long sought population of cold and dense aggregations with the potential of harbouring the earliest stages of massive star formation. Up to now there has been no systematic study on the temperature…
In this survey paper, we review recent uses of convolution neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding…
Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying PSF, small brightness variations in many sources, as well as…
With current wide-field near-infrared (NIR) instruments the scattered light in the near-infrared can be mapped over large areas. Below A_V ~ 10 the surface brightness is directly proportional to the column density, and at slightly higher…
Infrared dark clouds (IRDCs) contains cold dense gas at the earliest stage of massive star and cluster formation. In studying the IRDCs, a universal and fundamental task is to resolve their internal hierarchical structures. Various packages…
Despite increasing research in massive star formation, little is known about its earliest stages. Infrared Dark Clouds (IRDCs) are cold, dense and massive enough to harbour the sites of future high-mass star formation. But up to now, mainly…
The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the…
There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a…
This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level…
Infrared-dark clouds (IRDCs) harbor the early phases of cluster and high-mass star formation and are comprised of cold (~20 K), dense (n > 10$^4$ cm$^{-3}$) gas. The spectral energy distribution (SED) of IRDCs is dominated by the…
We present a brief review of the history of optical searches of galaxy clusters, starting from that of Abell. The traditional application of this survey method suffers from contamination due to projection of galaxies along the line of…
We develop a hybrid GNN-CNN architecture for the reconstruction of 3-dimensional continuous cosmological matter fields from discrete point clouds, provided by observed galaxy catalogs. Using the CAMELS hydrodynamical cosmological…
This letter proposes an improved CNN predictor (ICNNP) for reversible data hiding (RDH) in images, which consists of a feature extraction module, a pixel prediction module, and a complexity prediction module. Due to predicting the…
One of the most difficult jobs in remote sensing is dealing with traffic bottlenecks at airports. This fact has been confirmed by several studies attempting to resolve this issue. Among a wide range of approaches employed the most…
The Munich Near-IR Cluster Survey (MUNICS) is a wide-area, medium-deep, photometric survey selected in the K' band. The project's main scientific aims are the identification of galaxy clusters up to redshifts of unity and the selection of a…
Infrared dark clouds are kinematically complex molecular structures in the interstellar medium that can host sites of massive star formation. We present 4 square arcminute maps of the 12CO, 13CO, and C18O J = 3 to 2 lines from selected…
A large fraction of active galactic nuclei (AGN) are "invisible" in extant optical surveys due to either distance or dust-obscuration. The existence of this large population of dust-obscured, infrared-bright AGN is predicted by models of…
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-driven, highly representative, layered hierarchical image…
The Cosmic InfraRed Background (CIRB) is the sum total of the redshifted and reprocessed short wavelength radiation from the era of galaxy formation, and hence contains vital information about the history of galactic evolution. One of the…