A taxonomy of Deep Convolutional Neural Nets for Computer Vision [PDF]
Frontiers in Robotics and AI, 2016 Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features.
Suraj eSrinivas +5 more
doaj +6 more sources
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
ROADSIDE FOREST MODELING USING DASHCAM VIDEOS AND CONVOLUTIONAL NEURAL NETS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Tree failure is a primary cause of storm-related power outages throughout the United States. Roadside vegetation management is therefore critical to electric utility companies to prevent power outages during extreme weather conditions. It is difficult to
D. Joshi, C. Witharana
doaj +2 more sources
Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets With Multi-Stream Inputs [PDF]
IEEE Access, 2020 Understanding driver activity is vital for in-vehicle systems that aim to reduce the incidence of car accidents rooted in cognitive distraction. Automating real-time behavior recognition while ensuring actions classification with high accuracy is however
Chaoyun Zhang +4 more
doaj +2 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
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