Results 71 to 80 of about 11,320,128 (203)

Challenges in Hyperspectral Imaging for Autonomous Driving: The HSI-Drive Case

2025 15th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
The use of hyperspectral imaging (HSI) in autonomous driving (AD), while promising, faces many challenges related to the specifics and requirements of this application domain. On the one hand, non-controlled and variable lighting conditions, the wide depth-of-field ranges, and dynamic scenes with fast-moving objects. On the other hand, the requirements
Koldo Basterretxea, Jon Gutiérrez-Zaballa, Javier Echanobe   +2 more
openaire   +3 more sources

Hyperspectral image compression : adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding [PDF]

, 2008
Hyperspectral images present some specific characteristics that should be used by an efficient compression system. In compression, wavelets have shown a good adaptability to a wide range of data, while being of reasonable complexity.
Christophe, Emmanuel, Duhamel, Pierre, Mailhes, Corinne   +2 more
core   +1 more source

O129 CLASSIFICATION OF BARRETT’S CARCINOMA SPECIMENS BY HYPERSPECTRAL IMAGING (HSI)

Diseases of the Esophagus, 2019
Abstract Aim Hyperspectral imaging (HSI) technology combines imaging with spectroscopy and can be used for the classification of malignant and non-malignant cells. Thereby HSI combined with artificial intelligent algorithms can be used to predict tumor cells in in Barrett’s carcinoma specimens.
Thieme René, Maktabi Marianne, Köhler Hannes, Chalopin Claire, Jansen-Winkeln Boris, Gockel Ines   +5 more
openaire   +1 more source

HSI-IPGAN: Hyperspectral Image Inpainting via Generative Adversarial Network

, 2023
Abstract Due to the instability of the hyperspectral imaging system and the atmospheric interference, hyperspectral images (HSIs) often suffer from losing the image information of areas with different shapes, which significantly degrades the data quality and further limits the effectiveness of methods for subsequent tasks.
Hu Chen, Jia Li, Junjie Zhang, Liangang Zhang, Dan Zeng   +4 more
openaire   +1 more source

Guiding the AI revolution in periodontology and implant dentistry: Concepts, ethics, accountability, and a roadmap for sustainable adoption

Periodontology 2000, EarlyView.
Abstract Background Artificial intelligence (AI) is increasingly gaining attention in the field of periodontology and implant dentistry. Currently developed models can support diagnosis, treatment planning, and maintenance monitoring. However, most of the available literature is based on retrospective and often single‐modality data sets.
Aminollah Khormali, Negin Soghli, Patricia A. Miguez   +2 more
wiley   +1 more source

A clinically translatable hyperspectral endoscopy (HySE) system for imaging the gastrointestinal tract

Nature Communications, 2019
Hyperspectral imaging (HSI) enables recording both morphological and biochemical information, but image acquisition time and geometric distortions limit its clinical applicability.
Jonghee Yoon, James Joseph, Dale J. Waterhouse, A. Siri Luthman, George S. D. Gordon, Massimiliano di Pietro, Wladyslaw Januszewicz, Rebecca C. Fitzgerald, Sarah E. Bohndiek   +8 more
doaj   +1 more source

HyperGAN: A Hyperspectral Image Fusion Approach Based on Generative Adversarial Networks

Remote Sensing
The objective of hyperspectral pansharpening is to fuse low-resolution hyperspectral images (LR-HSI) with corresponding panchromatic (PAN) images to generate high-resolution hyperspectral images (HR-HSI).
Jing Wang, Xu Zhu, Linhai Jing, Yunwei Tang, Hui Li, Zhengqing Xiao, Haifeng Ding   +6 more
doaj   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

Determination of dry bean physiological maturity from UAS RGB time series using a stacking ensemble of color‐derived spectral features

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Evaluating physiological maturity is an important trait in dry bean breeding (Phaseolus vulgaris L.), but field scoring can be inaccurate, subjective, and a persistent bottleneck in multi‐environment trials. To address this, we developed a low‐cost, high‐throughput pipeline that predicts plot‐level days after planting at maturity from time ...
Aliasghar Bazrafkan, Lovepreet Singh, John Hawkins, Evan M. Wright, Valerio Hoyos‐Villegas   +4 more
wiley   +1 more source

NIRS Coupled With Machine Learning Algorithms for the Authentication and Quality Assessment of Indigenous Cattle and Buffalo Meat

eFood, Volume 7, Issue 5, October 2026.
Portable NIRS combined with machine learning enabled rapid and accurate authentication of cattle and buffalo meat. Optimized models (XGBoost, CNN) achieved > 98% accuracy, while SHAP revealed key spectral regions. This dual approach offers a field‐ready, reagent‐free tool for fraud prevention and traceability. ABSTRACT Ensuring the authenticity of high‐
Dip Ghosh, Raad Al Deen, Md. Abul Hashem
wiley   +1 more source