Results 71 to 80 of about 6,093,681 (207)

Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery

Remote Sensing, 2017
Convolutional neural networks (CNNs) have demonstrated their ability object detection of very high resolution remote sensing images. However, CNNs have obvious limitations for modeling geometric variations in remote sensing targets.
Zhaozhuo Xu, Xin Xu, Lei Wang, Rui Yang, Fangling Pu   +4 more
doaj   +1 more source

AML‐Net: Attention‐based multi‐scale lightweight model for brain tumour segmentation in internet of medical things

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Brain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some
Muhammad Zeeshan Aslam, Basit Raza, Muhammad Faheem, Aadil Raza   +3 more
wiley   +1 more source

An Improved Splicing Localization Method by Fully Convolutional Networks

IEEE Access, 2018
Liu and Pun proposed a method based on fully convolutional network (FCN) and conditional random field (CRF) to locate spliced regions in synthesized images from different source images. However, their work has two drawbacks: 1) FCN often smooths detailed
Beijing Chen, Xiaoming Qi, Yiting Wang, Yuhui Zheng, Hiuk Jae Shim, Yun-Qing Shi   +5 more
doaj   +1 more source

ECG‐TransCovNet: A hybrid transformer model for accurate arrhythmia detection using Electrocardiogram signals

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Abnormalities in the heart's rhythm, known as arrhythmias, pose a significant threat to global health, often leading to severe cardiac conditions and sudden cardiac deaths. Therefore, early and accurate detection of arrhythmias is crucial for timely intervention and potentially life‐saving treatment.
Hasnain Ali Shah, Faisal Saeed, Muhammad Diyan, Nouf Abdullah Almujally, Jae‐Mo Kang   +4 more
wiley   +1 more source

FCN-Based Carrier Signal Detection in Broadband Power Spectrum

IEEE Access, 2020
Carrier signal detection has been a problem for a long time, which is the first step for blind signal processing. In this paper, we propose a new method for carrier signal detection in the broadband power spectrum based on the fully convolutional network
Hao Huang, Jian-Qing Li, Jiao Wang, Hong Wang   +3 more
doaj   +1 more source

Individual bird identification by modelling temporal structure in bioacoustic embeddings

Methods in Ecology and Evolution, EarlyView.
Abstract Identifying individual animals from vocalizations is an emerging research area in computational bioacoustics. This non‐invasive approach reduces reliance on physical capture and tagging for wildlife monitoring. Recent advances in this area leverage deep learning and bioacoustic foundation models adapted from species‐level classifiers. However,
Jonathan Gallego, Juan D. Martinez‐Vargas, José D. López   +2 more
wiley   +1 more source

Automatic detection of cone photoreceptors with fully convolutional networks

, 2019
Purpose: To develop a fully automatic method, based on deep learning algorithms, for determining the locations of cone photoreceptors within adaptive optics scanning laser ophthalmoscope images and evaluate its performance against a dataset of manually ...
Alonso-Caneiro, D, Chen, FK, Sampson, DM, Hamwood, J, Collins, MJ   +4 more
core   +1 more source

Ship Detection Based on Improved R-FCN

Jisuanji kexue yu tansuo, 2020
Aiming at the problem of detecting different sizes and types of ships in complex sea conditions, a ship detection method based on deep learning is proposed, which is mainly for the improvement of regional fully convolutional networks (R-FCN).
HUANG Zhijun, SANG Qingbing
doaj   +1 more source

Data‐driven thresholds for standardized classification of severe Alzheimer's disease neuropathology using digital neuropathology

Brain Pathology, EarlyView.
We generated digital pathologic algorithms (AI‐based modules that can be freely shared) to: optimize the sampling (ROI) for diagnosis of ADNC‐related tau pathology; compare/transition between Aperio and HALO platforms; and rationalize severe Braak NFT staging, so that all Braak‐VI cases had dementia, and all Braak‐V cases had MCI or dementia.
Ryan K. Shahidehpour, Allison M. Neltner, Mitchell A. Klusty, Cole Corbett, Angelique D. Gonzalez, David A. Gutman, David W. Fardo, Adam D. Bachstetter, Cody Bumgardner, Margaret E. Flanagan, Peter T. Nelson   +10 more
wiley   +1 more source

A Survey of Methods for Constructing 3D Urban Models From Point Clouds

Computer Graphics Forum, EarlyView.
The survey outlines a general process for constructing 3D models from point clouds and categorizes the methods based on their use of templates, basic surface primitives, hybrid approaches, or linear primitives. Additionally, the survey reviews the datasets and benchmarks that are essential for testing and training the developed methods.
Chiara Romanengo, Silvia Biasotti, Bianca Falcidieno, Daniela Cabiddu, Elia Moscoso Thompson, Michela Spagnuolo   +5 more
wiley   +1 more source