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We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight…
We present 5 filter photometry of 21 carbon stars, 15 asteroids, 15 cataclysmic variables, 6 metal-poor stars, 5 Cepheids, 1775 field stars, blue horizontal branch (BHB) stars and RR Lyrae stars in the globular clusters M 15 and M 2, two…
We present mass models for a sample of 30 high-resolution rotation curves of low surface brightness (LSB) galaxies. We fit both pseudo-isothermal (core-dominated) and Cold Dark Matter (CDM) (cusp-dominated) halos for a wide variety of…
One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system. A key first step in reducing this…
We present a learning-based method for 6 DoF pose estimation of rigid objects in point cloud data. Many recent learning-based approaches use primarily RGB information for detecting objects, in some cases with an added refinement step using…
Due to advances in observation and imaging technologies, modern astronomical satellites generate large volumes of data. This necessitates efficient onboard data processing and high-speed data downlink. Reflecting this trend is the VERTECS…
We present a new classification method for quasar identification in the EROS-2 and MACHO datasets based on a boosted version of Random Forest classifier. We use a set of variability features including parameters of a continuous auto…
Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide…
The volume of data from current and future observatories has motivated the increased development and application of automated machine learning methodologies for astronomy. However, less attention has been given to the production of…
We assemble a database of 15 HSB and 15 LSB galaxies, for which surface brightness density and spectroscopic rotation curve data are both available and representative for various morphologies. We use this dataset to test the…
LAMOST DR5 released more than 200,000 low resolution spectra of early-type stars with S/N>50. Searching for metallic-line (Am) stars in such a large database and study of their statistical properties are presented in this paper. Six machine…
(ABRIDGED) Motivated by the desire to reliably and automatically classify structure of thousands of COSMOS galaxies, we present ZEST, the Zurich Estimator of Structural Types. To classify galaxy structure, ZEST uses: (i) Five non-parametric…
We present a machine learning (ML) based method for automated detection of Gamma-Ray Burst (GRB) candidate events in the range 60 keV - 250 keV from the AstroSat Cadmium Zinc Telluride Imager data. We use density-based spatial clustering to…
Deep learning techniques have been explored within the marine litter problem for approximately 20 years but the majority of the research has developed rapidly in the last five years. We provide an in-depth, up to date, summary and analysis…
CR-39 solid-state nuclear track detectors are widely used in fusion research for detecting charged particles produced in fusion reactions. However, analyzing increasingly complex and large-scale CR-39 track images to extract meaningful…
We present a non-parametric reconstruction of the rotation curves (RC) for 88 spiral galaxies under the LOESS+SIMEX technique. In order to compare methods we also perform the parametric approach assuming core and cuspy dark matter (DM)…
Availability of domain-specific datasets is an essential problem in object detection. Maritime vessel detection of inshore and offshore datasets is no exception, there is a limited number of studies addressing this need. For that reason, we…
Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework…
Mitigating data gaps in Gamma-ray bursts (GRBs) light curves (LCs) is crucial for cosmological research, enhancing the precision of parameters, assuming perfect satellite conditions for complete LC coverage with no gaps. This analysis…
Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and valuable information at global scale that can be used to…