Results 61 to 70 of about 5,326,339 (296)
Recurrent Fully Convolutional Networks for Video Segmentation [PDF]
2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 2017 Image segmentation is an important step in most visual tasks. While convolutional neural networks have shown to perform well on single image segmentation, to our knowledge, no study has been been done on leveraging recurrent gated architectures for video segmentation.
Sepehr Valipour +3 more
openaire +3 more sources
Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Conventional remote sensing image retrieval (RSIR) system usually performs single-label retrieval where each image is annotated by a single label representing the most significant semantic content of the image.
Zhenfeng Shao +4 more
doaj +1 more source
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 +7 more
core +1 more source
Deep Fully Convolutional Network for MR Fingerprinting [PDF]
, 2019 This work proposes an end-to-end deep fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employs linear dimensionality reduction and then uses a neural network to project the data into the tissue parameters.
Davies, Mike E. +4 more
core +1 more source
SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images [PDF]
One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification.
Akbari, Vahid +5 more
core +1 more source
Malaria parasite segmentation using U-Net: Comparative study of loss functions
Communications in Science and Technology, 2019 The convolutional neural network is commonly used for classification. However, convolutional networks can also be used for semantic segmentation using the fully convolutional network approach.
Julisa Bana Abraham
doaj +1 more source
Fully Convolutional Networks for Text Classification [PDF]
, 2018 In this work I propose a new way of using fully convolutional networks for classification while allowing for input of any size. I additionally propose two modifications on the idea of attention and the benefits and detriments of using the modifications.
openaire +4 more sources
Don't ignore Dropout in Fully Convolutional Networks
CoRR, 2019 Data for Image segmentation models can be costly to obtain due to the precision required by human annotators. We run a series of experiments showing the effect of different kinds of Dropout training on the DeepLabv3+ Image segmentation model when trained using a small dataset.
Thomas Spilsbury, Paavo Camps
openaire +3 more sources
SC-PNN: Saliency Cascade Convolutional Neural Network for Pansharpening
, 2021 In many remote sensing tasks, different types of regions or targets differ in requirements for spectral and spatial quality. The discrepancy reveals that a uniform pansharpening strategy applying to the entire image may not fulfill the varying demands of
Zhang, Jue +3 more
core +1 more source
Boundary‐Dependent Sleep–Wake Dysregulation in Idiopathic Hypersomnia
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Idiopathic hypersomnia (IH) presents with excessive daytime sleepiness (EDS) despite apparently preserved nocturnal sleep, challenging traditional models of hypersomnolence based on sleep loss or fragmentation. We aimed to test the hypothesis that EDS in IH reflects excessive stabilization of the sleep state, consistent with ...
Samantha Mombelli +13 more
wiley +1 more source