Results 91 to 100 of about 6,343,053 (206)

LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks

, 2019
In this work, a deep learning approach has been developed to carry out road detection by fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is first projected onto the camera image plane and then upsampled to obtain a set
Svensson, Lennart, Wahde, Mattias, Svensson, Lennart,, Bellone, Mauro,, Caltagirone, Luca, Wahde, Mattias,, Bellone, Mauro, Caltagirone, Luca,   +7 more
core   +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

Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018
Structural magnetic resonance imaging (sMRI) has been widely used for computer-aided diagnosis of neurodegenerative disorders, e.g., Alzheimer's disease (AD), due to its sensitivity to morphological changes caused by brain atrophy.
Chunfeng Lian, Ming-Xia Liu, Jun Zhang, D. Shen   +3 more
semanticscholar   +1 more source

Edge-Guided Non-Local Fully Convolutional Network for Salient Object Detection [PDF]

IEEE transactions on circuits and systems for video technology (Print), 2019
Fully Convolutional Neural Network (FCN) has been widely applied to salient object detection recently by virtue of high-level semantic feature extraction, but existing FCN-based methods still suffer from continuous striding and pooling operations leading
Zheng-Zheng Tu, Yan Ma, Chenglong Li, Jin Tang, B. Luo   +4 more
semanticscholar   +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

Multi-task Fully Convolutional Network for Brain Tumour Segmentation

, 2017
In this paper, a novel, multi-task fully convolutional network (FCN) architecture is proposed for automatic segmentation of brain tumour. The proposed network builds on the hierarchical relationship between tumour substructures with branch and leaf ...
Haocheng Shen, Zhang, Jianguo, Ruixuan Wang, Jianguo Zhang, McKenna, Stephen; id_orcid, Stephen McKenna, Shen, Haocheng, Wang, Ruixuan   +7 more
core   +1 more source

Brain tissue classification in hyperspectral images using multistage diffusion features and transformer

Journal of Microscopy, EarlyView.
Abstract Brain surgery is a widely practised and effective treatment for brain tumours, but accurately identifying and classifying tumour boundaries is crucial to maximise resection and avoid neurological complications. This precision in classification is essential for guiding surgical decisions and subsequent treatment planning.
Neetu Sigger, Tuan T. Nguyen, Gianluca Tozzi   +2 more
wiley   +1 more source

FM-FCN: A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

Research
Neural networks excel at capturing local spatial patterns through convolutional modules, but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological signals.
Fangfang Zhu, Qichao Niu, Xiang Li, Qi Zhao, Honghong Su, Jianwei Shuai   +5 more
doaj   +1 more source

Adaptive Prototype‐Based Few‐Shot Segmentation for Debris Flow Early Warning

Journal of Flood Risk Management, Volume 19, Issue 4, December 2026.
From single‐parameter monitoring to a cross‐modal few‐shot visual paradigm for debris flow early warning. Conventional rainfall‐gauge and mud‐level monitoring is data‐sparse, delayed in response, and spatially limited. The proposed framework instead fuses a support set, a query image, and a textual target category through ResNet‐50 and CLIP encoders ...
Mingzhi Zhang, Yueping Yin, Chun Bao, Rui Li, Cuiqiong Zhou, Zhikun Hu, Frank Mugagga, Yazidhi Bamutaze   +7 more
wiley   +1 more source