Results 61 to 70 of about 49,813 (252)

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

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

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

A new bandwidth selection criterion for using SVDD to analyze hyperspectral data

, 2019
This paper presents a method for hyperspectral image classification that uses support vector data description (SVDD) with the Gaussian kernel function. SVDD has been a popular machine learning technique for single-class classification, but selecting the ...
Baumgardner, Chaudhuri, Evangelista, Kakde, Kakde, Peredriy, Walter   +6 more
core   +1 more source

Classification for hyperspectral imaging [PDF]

, 2014
Hyperspectral Imaging is a method of collecting and processing the information across pre-defined electromagnetic spectrum. These measurements make it possible to derive a continuous spectrum for each pixel of the image. After necessary adjustments these image spectra can be compared with database of reflectance spectra in order to recognise tested ...
Polak, Adam, Marshall, Stephen, Ren, Jinchang, Stothard, David J.M.   +3 more
openaire  

Hyperspectral Image Classification via Kernel Sparse Representation [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2011
In this paper, a new technique for hyperspectral image classification is proposed. Our approach relies on the sparse representation of a test sample with respect to all training samples in a feature space induced by a kernel function. Projecting the samples into the feature space and kernelizing the sparse representation improves the separability of ...
Yi Chen, Nasser M. Nasrabadi, Trac D. Tran   +2 more
openaire   +1 more source

Bayesian Gravitation-Based Classification for Hyperspectral Images

IEEE Transactions on Geoscience and Remote Sensing, 2022
Integration of spectral and spatial information is extremely important for the classification of high-resolution hyperspectral images (HSIs). Gravitation describes interaction among celestial bodies which can be applied to measure similarity between data for image classification.
Aizhu Zhang, Genyun Sun, Zhaojie Pan, Jinchang Ren, Xiuping Jia, Chenglong Zhang, Hang Fu, Yanjuan Yao   +7 more
openaire   +2 more sources

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

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

High Dimensional Feature for Hyperspectral Image Classification

MATEC Web of Conferences, 2018
Making a high dimensional (e.g., 100k-dim) feature for hyperspectral image classification seems not a good idea because it will bring difficulties on consequent training, computation, and storage.
Wang Cailing, Wang Hongwei, Zhang Yinyong, Wen Jia, Yang Fan   +4 more
doaj   +1 more source