Results 31 to 40 of about 1,980,897 (317)
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
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit.
Liang-Chieh Chen +4 more
semanticscholar +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
SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition
IET Image Processing, 2023 Although most state‐of‐the‐art action recognition models have adopted a two‐stream 3D convolutional structure as a backbone network, few works have studied the impact of loss functions on action recognition models.
Zhaoqilin Yang +4 more
doaj +1 more source
LiteDEKR: End‐to‐end lite 2D human pose estimation network
IET Image Processing, 2023 The 2D human pose estimation plays an important role in human‐computer interaction and action recognition. Although the method based on high‐resolution network has superior performance, there is still room for improvement in terms of speed and ...
Xueqiang Lv +5 more
doaj +1 more source
Deep Net Tree Structure for Balance of Capacity and Approximation Ability
Frontiers in Applied Mathematics and Statistics, 2019 Deep learning has been successfully used in various applications including image classification, natural language processing and game theory. The heart of deep learning is to adopt deep neural networks (deep nets for short) with certain structures to ...
Charles K. Chui +4 more
doaj +1 more source
Convolutional neural networks for the recognition of food related environments [PDF]
, 2016 Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Petia RadevaIn this work we are going to experiment with convolutional neural networks with the objective of recognizing food ...
Llorente Pineda, Edgar
core +6 more sources
RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation [PDF]
2021 Digital Image Computing: Techniques and Applications (DICTA), 2021 Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back to examine the real need for such complexity.
Tariq Mahmood Khan +2 more
openaire +3 more sources
Communication-Optimal Convolutional Neural Nets
CoRR, 2018 Efficiently executing convolutional neural nets (CNNs) is important in many machine-learning tasks. Since the cost of moving a word of data, either between levels of a memory hierarchy or between processors over a network, is much higher than the cost of an arithmetic operation, minimizing data movement is critical to performance optimization.
James Demmel, Grace Dinh
openaire +2 more sources
Multi‐objective single‐shot neural architecture search via efficient convolutional filters
Electronics Letters, 2023 This paper presents a novel approach for fast neural architecture search (NAS) in Convolutional Neural Networks (CNNs) for end‐to‐end License Plate Recognition (LPR).
Seyed Mahdi Shariatzadeh +2 more
doaj +1 more source