Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy [PDF]
, 2019 Neural circuits can be reconstructed from brain images acquired by serial section electron microscopy. Image analysis has been performed by manual labor for half a century, and efforts at automation date back almost as far.
Kisuk Lee +5 more
core +5 more sources
Region-Based Convolutional Neural Nets for Localization of Glomeruli in Trichrome-Stained Whole Kidney Sections. [PDF]
J Am Soc Nephrol, 2018 Bukowy JD +8 more
europepmc +2 more sources
CAE-CNN-Based DOA Estimation Method for Low-Elevation-Angle Target
Remote Sensing, 2022 For the DOA (direction of arrival) estimation of a low-elevation-angle target under the influence of a multipath effect, this paper proposes a DOA estimation method based on CAE (convolutional autoencoder) and CNN (convolutional neural network).
Fangzheng Zhao +3 more
doaj +1 more source
CDF‐net: A convolutional neural network fusing frequency domain and spatial domain features
IET Computer Vision, 2023 Convolutional neural network (CNN), as a classic deep learning algorithm, has been applied to various computer vision tasks. However, most classic CNN models focus on the extraction and utilisation of spatial domain features, while ignoring the potential
Aitao Yang +7 more
doaj +1 more source
Microstrip antenna modelling based on image‐based convolutional neural network
Electronics Letters, 2023 Convolutional neural networks (CNN) have a strong feature extraction ability for images and present a high level of efficiency and accuracy in object detection and image recognition.
Hao Fu +4 more
doaj +1 more source
Convex Relaxations of Convolutional Neural Nets [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 We propose convex relaxations for convolutional neural nets with one hidden layer where the output weights are fixed. For convex activation functions such as rectified linear units, the relaxations are convex second order cone programs which can be solved very efficiently.
Bartan, Burak, Pilanci, Mert
openaire +2 more sources
Rumour Detection Based on Graph Convolutional Neural Net [PDF]
IEEE Access, 2021 Rumor detection is an important research topic in social networks, and lots of rumor detection models are proposed in recent years. For the rumor detection task, structural information in a conversation can be used to extract effective features. However, many existing rumor detection models focus on local structural features while the global structural
Na Bai +3 more
openaire +2 more sources
A method for superfine pavement crack continuity detection based on topological loss
Electronics Letters, 2023 Deep convolutional neural networks have become a popular tool for the automatic detection of pavement cracks. Despite their widespread use, the models currently available tend to emphasize pixel‐level classification accuracy for cracks, often overlooking
Guohui Jia +4 more
doaj +1 more source
Human stability assessment and fall detection based on dynamic descriptors
IET Image Processing, 2023 Fall detection systems use a number of different technologies to achieve their goals. This way, they contribute to better life conditions for the elderly community.
Jesús Gutiérrez +2 more
doaj +1 more source
IET Intelligent Transport Systems, 2023 Rail transit has many advantages, such as large passenger capacity, convenience, safety, and environmental protection, making it the preferred travel mode for most passengers.
Xuanrong Zhang +3 more
doaj +1 more source