Results 61 to 70 of about 6,093,681 (207)
Fully Convolutional Neural Network with Relation Aware Context Information for Image Parsing
, 2021 Image parsing is among the core tasks in the field of computer vision. The automatic pixel-wise segmentation offers great potential in terms of application adaptability.
Azam, Basim +5 more
core +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Ice concentration estimates are typically acquired from algorithms using passive microwave satellite data, and from image analysis charts, but these have limitations. Estimates acquired from passive microwave data have coarse spatial resolution, may have
Keerthijan Radhakrishnan +2 more
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +1 more source
Tire Defect Detection Using Fully Convolutional Network
IEEE Access, 2019 A deep convolutional neural network has recently witnessed rapid progress due to the strong feature learning capability. In this paper, we focus on its application in the industrial field and propose a method based on a fully convolutional network (FCN ...
Ren Wang +3 more
doaj +1 more source
Remote Sensing, 2021 Current mainstream networks for hyperspectral image (HSI) classification employ image patches as inputs for feature extraction. Spatial information extraction is limited by the size of inputs, which makes networks unable to perform effective learning and
Qian Liu +4 more
doaj +1 more source
Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.Abstract Purpose To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Methods Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer‐reviewed studies ...
Amna Gillani +5 more
wiley +1 more source
Diagnosis of Portal Hypertension: Advancing Towards Non‐Invasive Solutions
Portal Hypertension &Cirrhosis, EarlyView.This review systematically summarizes a full spectrum of non‐invasive diagnostic approaches for portal hypertension (PH), including imaging modalities, elastography, serum biomarkers, composite scoring systems and endoscopic ultrasound‐guided portal pressure gradient (EUS‐PPG), and analyzes their performance across different liver disease etiologies ...
Lijia Yin, Huikuan Chu, Ling Yang
wiley +1 more source
Look-behind fully convolutional neural network for computer-aided endoscopy
, 2019 In this paper, we propose a novel Fully Convolutional Neural Network (FCN) architecture aiming to aid the detection of abnormalities, such as polyps, ulcers and blood, in gastrointestinal (GI) endoscopy images.
Diamantis D.E., Iakovidis D.K., Koulaouzidis A.
core +1 more source
Integrating Image Segmentation and Deep Learning to Improve Radio Frequency Propagation Models
International Journal of Satellite Communications and Networking, EarlyView.ABSTRACT This paper proposes a multi‐sensor approach to improve radio frequency (RF) propagation models, which play a key role in the rapidly expanding field of connected vehicle technology. Focusing on the 1‐ to 20‐GHz frequency range, which is critical for both satellite‐to‐vehicle and base station‐to‐vehicle communications, our study introduces a ...
Jonathan Israel +2 more
wiley +1 more source