Results 121 to 130 of about 153,077 (360)

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

Self- and Cross-Attention Enhanced Transformer for Visible and Thermal Infrared Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Visible hyperspectral image (V-HSI) and thermal infrared hyperspectral image (TI-HSI) have been crucial data sources for land cover classification. V-HSI can directly provide information of land surface, such as shape, color, texture, and other features.
Enyu Zhao, Yongfang Su, Nianxin Qu, Yulei Wang, Caixia Gao, Jian Zeng   +5 more
doaj   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Classification techniques for hyperspectral remote sensing [PDF]

, 2011
This study concerns with classification techniques in high dimensional space such as that of Hyperspectral Imaging (HSI) data sets, with objectives of understanding the strength and weakness of various classifiers and at the same time to study how ...
Kam, Firmin
core  

Real‐Time Multicolor Fluorescence Microscopy via Cross‐Channel Acquisition and Deep‐Learning‐Based Inference

Advanced Intelligent Discovery, EarlyView.
Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto, Yuxin Jin, Nicholas Roy, Sarthak Misra   +3 more
wiley   +1 more source

Spectral-Spatial Attention Networks for Hyperspectral Image Classification

Remote Sensing, 2019
Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks.
Hao Sun, Xiangtao Zheng, Xiaoqiang Lu, Siyuan Wu   +3 more
semanticscholar   +1 more source

Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]

, 2013
Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Eches, Olivier, Benediktsson, Jon Atli, Dobigeon, Nicolas, Tourneret, Jean-Yves   +3 more
core   +1 more source

Toward Intelligent Multimodal Holography for Real‐Time Chemical Imaging of Dynamic Ion Separation

Advanced Intelligent Discovery, EarlyView.
Intelligent multimodal holography integrates digital off‐axis holography, spectroscopic imaging, and AI‐driven reconstruction to visualize ion transport and chemical dynamics in real time. In this perspective paper, we outline how this approach enables label‐free, chemically specific monitoring of complex environments and discuss its potential to ...
Giovanna Ricchiuti, Sarah I. Allec, Maxim Ziatdinov, Venkateshkumar Prabhakaran   +3 more
wiley   +1 more source

Hyperspectral Image Resolution Enhancement Based on Spectral Unmixing and Information Fusion

, 2011
Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels.
Avbelj, Janja, Reinartz, Peter, Müller, Rupert, Cerra, Daniele, Bieniarz, Jakub   +4 more
core  

Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery [PDF]

, 2010
This paper studies a new Bayesian unmixing algorithm for hyperspectral images. Each pixel of the image is modeled as a linear combination of so-called endmembers.
Eches, Olivier, Dobigeon, Nicolas, Tourneret, Jean-Yves, Mailhes, Corinne   +3 more
core   +1 more source