When confronted with a new dataset, the application of AI-based segmentation methods is non-trivial. Substantial manual effort is required to optimize the network topology, preprocessing, training procedure and inference strategy to the task at hand. Such adaptations are non-trivial and require substantial expertise to ensure optimal performance on each dataset. …

PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms

PickYOLO is a super-fast, universal particle detector, based on the deep-learning real-time object recognition system YOLO (You Only Look Once). After training with the centre coordinates of a few hundred representative particles, the network automatically detects additional particles …

preCICE is an open-source coupling library for partitioned multi-physics simulations, including, but not restricted to fluid-structure interaction and conjugate heat transfer simulations.

Partitioned means that preCICE couples existing programs/solvers capable of simulating a subpart of the complete physics involved in a simulation. This allows for the high flexibility that is …

Standard neural networks for imaging tasks are tied to a certain fixed image resolution they were trained on. Neural operators were proposed in Kovachki, Nikola, et al. “Neural operator: Learning maps between function spaces with applications to PDEs” as a way to generalize neural networks to an infinite-dimensional setting. A …

2D image registration tool using CV2 SIFT.

The solar wind is constituted by plasma and radiation emitted by the Sun and it can have adverse effects on power grids, telecommunication infrastructure and space assets. In particular during late October-November 2003, geomagnetic storms (popularly known as “Halloween storms”) resulted in numerous satellite anomalies, including the rerouting of transpolar …

Syris (synchrotron radiation imaging simulation) is a framework for simulations of X-ray absorption and phase contrast dynamic imaging experiments, like time-resolved radiography, tomography or laminography. It includes X-ray sources, various sample shape creation possibilities, complex refractive index lookup options, motion model and indirect detection model …

The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting …

This repository contains Python data processing scripts to be used with the UFO framework. At the moment they are targeted at high-performance reconstruction of tomographic data sets including phase retrieval and artifact reduction.

This python based tool extracts metadata from machine generated scanning microscopy images in the TIFF format. Currently only metadata from SEMs manufactured by Zeiss, and acquired by their own software is supported. In future, metadata from machines developed by other manufacturers, and produced by other softwares would also be included. …

A versatile, agnostic, and configurable tool was developed to generate instance-segmented imaging datasets of nanoparticles. The synthetic generator tool employs domain randomization to expand the image/mask pairs dataset for training supervised deep learning models. The approach eliminates tedious manual annotation and allows the training of high-performance models for microscopy image …

This project focuses on the deep learning-based automatic analysis of polymer electrolyte membrane water electrolyzers (PEMWE) oxygen evolution videos. This repository contains the Python implementation of the UTILE-Oxy software for automatic video analysis, feature extraction, and plotting.

The models we present in this work are trained on a specific use-case …

In materials science research, digital volume correlation (DVC) analysis is commonly used to track deformations and strains to elucidate morphology-function relationships. Optical flow-based DVC is particularly popular because of its robustness to estimate the correlation as a dense deformation vector. Recently, computer vision researchers showed that network-based optical flow approaches …