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Object pose estimation is a key perceptual capability in robotics. We propose a fully-convolutional extension of the PoseCNN method, which densely predicts object translations and orientations. This has several advantages such as improving…
Convolutional neural networks (CNNs) have achieved remarkable performance in various fields, particularly in the domain of computer vision. However, why this architecture works well remains to be a mystery. In this work we move a small step…
In the near-future, dedicated telescopes observe Earth-like exoplanets in reflected light, allowing their characterization. Because of the huge distances, every exoplanet will be a single pixel, but temporal variations in its spectral flux…
The main challenges of using electroencephalogram (EEG) signals to make eye-tracking (ET) predictions are the differences in distributional patterns between benchmark data and real-world data and the noise resulting from the unintended…
Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering transform, a statistical tool sharing ideas with…
We have developed convolutional neural networks (CNN) for a facial expression recognition task. The goal is to classify each facial image into one of the seven facial emotion categories considered in this study. We trained CNN models with…
Constancy and anisotropy of vacuum refractive index serves as a strong way to probe the predictions of theories beyond the standard model (BSM), especially those that predict breaking of local Lorentz and CPT symmetries. For photons of…
Single photon emission computed tomography (SPECT) is a well established clinical tool for functional imaging. A limitation of current SPECT systems is the use of mechanical collimation, where only a small fraction of the emitted photons is…
A major issue in optical astronomical image analysis is the combined effect of the instrument's point spread function (PSF) and the atmospheric seeing that blurs images and changes their shape in a way that is band and time-of-observation…
Study of high energy cosmic events in the MeV range requires detector with high efficiency and energy resolution to be constructed. The present setup consisting of scintillator crystals CeBr3 with different thickness, each coupled with 12 x…
A large volume TPC is a leading candidate for the central tracking detector at a future high energy linear collider. To improve the resolution a new readout based on micro-pattern gas detectors is being developed. Measurements of the…
Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images. For example, most popular datasets in computer vision contain images much less than a megapixel in size (0.09MP for…
Electron-beam propagation experiments have been used to determine the energy and angle dependence of electron-electron (ee) scattering a two-dimensional electron gas (2DEG) in a very direct manner by a new spectroscopy method. The…
Applying convolutional neural networks to large images is computationally expensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is capable of…
A novel image analysis algorithm as applied to images of Nuclear Track Detectors (NTD) is presented. This process, involving sequential application of deconvolution and convolution techniques, followed by the application of Artificial…
A semiconductor tracker for muon scattering tomography is presented. The tracker contains silicon strip sensors with an $80\,\mu$m pitch, precision mechanics and integrated cooling. The electronic readout of the sensors is performed by a…
Organic scintillators are important in advancing nuclear detection and particle physics experiments. Achieving a high signal-to-noise ratio necessitates efficient pulse shape discrimination techniques to accurately distinguish between…
3D Compton scattering imaging is an upcoming concept exploiting the scattering of photons induced by the electronic structure of the object under study. The so-called Compton scattering rules the collision of particles with electrons and…
Objects in aerial images have greater variations in scale and orientation than in typical images, so detection is more difficult. Convolutional neural networks use a variety of frequency- and orientation-specific kernels to identify objects…
The methods to measure the polarization of the x-rays from highly charged heavy ions with a significantly higher accuracy than the existing technology is needed to explore relativistic and quantum electrodynamics (QED) effects including the…