31 citations · 66 across the 5 of their papers we have counts for
5 papers
Machine-learned models for magnetic materials
Paweł Leszczyński, Kamil Kutorasiński, Marcin Szewczyk +1
We present a general framework for modeling power magnetic materials characteristics using deep neural networks. Magnetic materials represented by multidimensional characteristics…
Self-Normalized Density Map (SNDM) for Counting Microbiological Objects
Krzysztof M. Graczyk, Jaroslaw Pawlowski, Sylwia Majchrowska +1
The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U-Net. Two statistical metho…
Generation of microbial colonies dataset with deep learning style transfer
Jarosław Pawłowski, Sylwia Majchrowska, Tomasz Golan
We introduce an effective strategy to generate an annotated synthetic dataset of microbiological images of Petri dishes that can be used to train deep learning models in a fully su…
Deep neural networks approach to microbial colony detection -- a comparative analysis
Sylwia Majchrowska, Jarosław Pawłowski, Natalia Czerep +3
Counting microbial colonies is a fundamental task in microbiology and has many applications in numerous industry branches. Despite this, current studies towards automatic microbial…
AGAR a microbial colony dataset for deep learning detection
Sylwia Majchrowska, Jarosław Pawłowski, Grzegorz Guła +7
The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on agar plates. It contains 18000 photos of five different microorg…