Results 91 to 100 of about 109,457 (327)

HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis

Remote Sensing
The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective.
Daniel La’ah Ayuba, Jean-Yves Guillemaut, Belen Marti-Cardona, Oscar Mendez   +3 more
doaj   +1 more source

Theoretical Principles and Perspectives of Hyperspectral Imaging Applied to Sediment Core Analysis [PDF]

, 2022
Kévin Jacq, Maxime Debret, Bernard Fanget, Didier Coquin, Pierre Sabatier, Cécile Pignol, Fabien Arnaud, Yves Perrette   +7 more
openalex   +1 more source

Real‐Time In Vivo Monitoring of Anastomotic Intestinal Ischemia Using Implantable Resorbable Organic Sensors

Advanced Science, EarlyView.
Resorbable impedance sensors are successfully implanted into porcine small intestinal anastomoses. Impedance was recorded for 2 hours prior, and 2 hours following ischemia induction, and a significant drop in tissue impedance was observed. Abstract Anastomotic failure remains one of the most severe complications in gastrointestinal surgery.
Dennis Wahl, Finn Jaekel, Julia Henne, Richard Kantelberg, Daniel C. Freund, Eberhard Grambow, Brigitte Vollmar, Amelie R. Zitzmann, Hans Kleemann, Jochen Hampe, Sebastian Hinz, Clemens Schafmayer, Karl Leo   +12 more
wiley   +1 more source

Fast Spectral Clustering for Unsupervised Hyperspectral Image Classification

Remote Sensing, 2019
Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance.
Yang Zhao, Yuan Yuan, Qi Wang
doaj   +1 more source

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 Method for Finding Structured Sparse Solutions to Non-negative Least Squares Problems with Applications [PDF]

, 2013
Demixing problems in many areas such as hyperspectral imaging and differential optical absorption spectroscopy (DOAS) often require finding sparse nonnegative linear combinations of dictionary elements that match observed data.
Esser, Ernie, Lou, Yifei, Xin, Jack
core  

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

DEEP NO LEARNING APPROACH FOR UNSUPERVISED CHANGE DETECTION IN HYPERSPECTRAL IMAGES [PDF]

, 2021
Sudipan Saha, Lukas Kondmann, Xiao Xiang Zhu   +2 more
openalex   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
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