Related papers: Duchamp: a 3D source finder for spectral-line data
We present Morpheus, a new model for generating pixel-level morphological classifications of astronomical sources. Morpheus leverages advances in deep learning to perform source detection, source segmentation, and morphological…
In this work we address the problem of simultaneous multi-frequency detection of extragalactic point sources in maps of the Cosmic Microwave Background. We apply a new linear filtering technique, the so called `matched matrix filters', that…
We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…
Multiview pedestrian detection typically involves two stages: human modeling and pedestrian localization. Human modeling represents pedestrians in 3D space by fusing multiview information, making its quality crucial for detection accuracy.…
A method for spatial deconvolution of spectra is presented. It follows the same fundamental principles as the ``MCS image deconvolution algorithm'' (Magain, Courbin, Sohy, 1998) and uses information contained in the spectrum of a reference…
We review the well-known matched filter method for the detection of point sources in astronomical images. This is shown to be optimal (that is, to saturate the Cramer--Rao bound) under stated conditions that are very strong: an isolated…
Over 3 billion astronomical objects have been detected in the more than 22 million orthogonal transfer CCD images obtained as part of the Pan-STARRS1 $3\pi$ survey. Over 85 billion instances of those objects have been automatically detected…
A numerical method is developed for recovering both the source locations and the obstacle from the scattered Cauchy data of the time-harmonic acoustic field. First of all, the incident and scattered components are decomposed from the…
3D data derived from satellite images is essential for scene modeling applications requiring large-scale coverage or involving locations not accessible by airborne lidar or cameras. Measuring the resolution of this data is important for…
Point-source contamination in high-precision Cosmic Microwave Background (CMB) maps severely affects the precision of cosmological parameter estimates. Among the methods that have been proposed for source detection, wavelet techniques based…
High speed, high-resolution, and accurate 3D scanning would open doors to many new applications in graphics, robotics, science, and medicine by enabling the accurate scanning of deformable objects during interactions. Past attempts to use…
Acoustic source localization has been applied in different fields, such as aeronautics and ocean science, generally using multiple microphones array data to reconstruct the source location. However, the model-based beamforming methods fail…
Computer vision algorithms are powerful tools in astronomical image analyses, especially when automation of object detection and extraction is required. Modern object detection algorithms in astronomy are oriented towards detection of stars…
Neutrino telescopes are moving steadily toward the goal of detecting astrophysical neutrinos from the most powerful galactic and extragalactic sources. Here we describe analysis methods to search for high energy point-like neutrino sources…
This paper proposes a novel feature called spectrum congruency for describing edges in images. The spectrum congruency is a generalization of the phase congruency, which depicts how much each Fourier components of the image are congruent in…
We present preliminary results from our on-going study: Comparing and optimizing source detection procedures for XMM images. By constructing realistic spatial and spectral source distributions and ``observing'' these through the XMM Science…
This paper develops a mathematical theory of super-resolution. Broadly speaking, super-resolution is the problem of recovering the fine details of an object---the high end of its spectrum---from coarse scale information only---from samples…
The increasing volume of gamma-ray data demands new analysis approaches that can handle large-scale datasets while providing robustness for source detection. We present a Deep Learning (DL) based pipeline for detection, localization, and…
In this paper we describe an unmanned aerial system equipped with a thermal-infrared camera and software pipeline that we have developed to monitor animal populations for conservation purposes. Taking a multi-disciplinary approach to tackle…
In this paper we consider the problem of localizing a set of broadband sources from a finite window of measurements. In the case of narrowband sources this can be reduced to the problem of spectral line estimation, where our goal is simply…