Results 91 to 100 of about 77,081 (273)

Real‐Time High‐Definition Hyperspectral Endoscopy via Spatial‐Temporal Low‐Frequency‐Stochastic Spectral Encoding

Advanced Science, EarlyView.
A real‐time, high‐definition hyperspectral endoscopy is enabled by developing a spatial‐temporal spectral encoding approach based on low‐frequency stochastic filters combined with an encoding‐guided attention network. It provides hyperspectral image of in vivo tissue with fine superficial features, enables visualization of rapid and subtle ...
Xiaowei Liu, Jiakang Shao, Julin Xiao, Chenying Yang, Jiahe Zhang, Xiaoyu Yang, Xiang Hao, Ying Gu, Xu Liu, Yizhou Tan, Ji Qi, Qing Yang   +11 more
wiley   +1 more source

A Hyperspectral Imaging Approach to White Matter Hyperintensities Detection in Brain Magnetic Resonance Images

Remote Sensing, 2017
White matter hyperintensities (WMHs) are closely related to various geriatric disorders including cerebrovascular diseases, cardiovascular diseases, dementia, and psychiatric disorders of elderly people, and can be generally detected on T2 weighted (T2W)
Hsian-Min Chen, Hsin Che Wang, Jyh-Wen Chai, Chi-Chang Clayton Chen, Bai Xue, Lin Wang, Chunyan Yu, Yulei Wang, Meiping Song, Chein-I Chang   +9 more
doaj   +1 more source

Generalized Inpainting Method for Hyperspectral Image Acquisition

, 2015
A recently designed hyperspectral imaging device enables multiplexed acquisition of an entire data volume in a single snapshot thanks to monolithically-integrated spectral filters.
Blanch, Carolina, Cambareri, Valerio, Degraux, Kévin, Geelen, Bert, Image Processing (ICIP), 2015 IEEE International Conference on, Jacques, Laurent, Lafruit, Gauthier   +6 more
core   +1 more source

Generative Artificial Intelligence Shaping the Future of Agri‐Food Innovation

AgriFood: Journal of Agricultural Products for Food, EarlyView.
Emerging use cases of generative artificial intelligence in agri‐food innovation. ABSTRACT The recent surge in generative artificial intelligence (AI), typified by models such as GPT, diffusion models, and large vision‐language architectures, has begun to influence the agri‐food sector.
Jun‐Li Xu, Dimitrios Argyropoulos, Tamíris da Costa   +2 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

TEBS: Temperature–Emissivity–Driven band selection for thermal infrared hyperspectral image classification with structured State-Space model and gated attention

International Journal of Applied Earth Observations and Geoinformation
Thermal infrared hyperspectral images (TIR-HSIs) provide unique spectral insights that are often unattainable with visible imagery, making them invaluable for applications such as land cover classification and geological mapping.
Enyu Zhao, Nianxin Qu, Yulei Wang, Caixia Gao, Jian Zeng   +4 more
doaj   +1 more source

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

Harmonizing ground and UAV hyperspectral data: A novel spectral correction method for maximizing estimation models and datasets of ground hyperspectral

Smart Agricultural Technology
The accurate and effective monitoring of rice nitrogen status using hyperspectral datasets and estimation models is important for precision agriculture and intelligent breeding.
Zhonglin Wang, Pengxin Deng, Kairui Chen, Ying Xiong, Feng Yang, Cheng Wang, Zhixin Li, Biao Li, Yongjian Sun, Zongkui Chen, Zhiyuan Yang, Jun Ma   +11 more
doaj   +1 more source

Detection of gold nano-particles in atherosclerotic plaque using hyperspectral X-ray imaging [PDF]

, 2017
C
Boone, Matthieu, Chhour, Peter, Cormode, David, Egan, Christopher, Heyndrickx, Marjolein, Van Assche, Frederic, Van Hoorebeke, Luc, Vincze, Laszlo   +7 more
core   +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