Critical review of processing and classification techniques for images and spectra in microplastic research
Microplastic research is a rapidly developing field, with urgent needs for high throughput
and automated analysis techniques. We conducted a review covering image analysis from …
and automated analysis techniques. We conducted a review covering image analysis from …
[Retracted] Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm
Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural
network denoising SRS photos: hyperspectral resolution enhancement and denoising one …
network denoising SRS photos: hyperspectral resolution enhancement and denoising one …
[HTML][HTML] DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation
Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …
Environmentally Friendly Approach to the Reduction of Microplastics during Domestic Washing: Prospects for Machine Vision in Microplastics Reduction
AP Periyasamy - Toxics, 2023 - mdpi.com
The increase in the global population is directly responsible for the acceleration in the
production as well as the consumption of textile products. The use of textiles and garment …
production as well as the consumption of textile products. The use of textiles and garment …
Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers
have to accomplish to assess the effects of different experimental conditions on biological …
have to accomplish to assess the effects of different experimental conditions on biological …
An adaptive genetic algorithm as a supporting mechanism for microscopy image analysis in a cascade of convolution neural networks
D Połap - Applied Soft Computing, 2020 - Elsevier
The analysis of microscopy images allows for making the description of given samples
containing different microscopic organisms. It is important due to the life phase analysis of …
containing different microscopic organisms. It is important due to the life phase analysis of …
Quantitative evaluation of ImageJ thresholding algorithms for microbial cell counting
L Nichele, V Persichetti, M Lucidi, G Cincotti - Osa Continuum, 2020 - opg.optica.org
Binarization is a key process in microscopy cell counting and cytometry analysis that is
performed before segmentation to identify a cell within the background. We test the …
performed before segmentation to identify a cell within the background. We test the …
Extraction, characterisation and remediation of microplastics from organic solid matrices
VSNS Goli, EK Paleologos, A Farid… - Environmental …, 2022 - icevirtuallibrary.com
Plastics are an essential commodity due to their superior engineering properties, durability
and low cost for utilization in various commercial products. However, the degradation of …
and low cost for utilization in various commercial products. However, the degradation of …
Synthetic image rendering solves annotation problem in deep learning nanoparticle segmentation
Nanoparticles occur in various environments as a consequence of man‐made processes,
which raises concerns about their impact on the environment and human health. To allow for …
which raises concerns about their impact on the environment and human health. To allow for …
Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy
Abstract Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images
and the corresponding ground-truth annotations, designed to foster innovative research in …
and the corresponding ground-truth annotations, designed to foster innovative research in …