Results 31 to 40 of about 42,190 (170)
Applied Sciences, 2021 Recently, hyperspectral image (HSI) classification using deep learning has been actively studied using 2D and 3D convolution neural networks (CNN). However, they learn spatial information as well as spectral information.
Byungjin Kang +3 more
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EURASIP Journal on Image and Video Processing, 2018 UAV remote sensing has been widely used in emergency rescue, disaster relief, environmental monitoring, urban planning, and so on. Image recognition and image location in environmental monitoring has become an academic hotspot in the field of computer ...
Kunrong Zhao +6 more
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Visual tracking based on transfer learning of deep salience information
Opto-Electronic Advances, 2020 In this paper, we propose a new visual tracking method in light of salience information and deep learning. Salience detection is used to exploit features with salient information of the image.
Zuo Haorui +3 more
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Optimized Layered Convolutional Sub-health Recognition Algorithm of Improved Capsule Network
Jisuanji kexue yu tansuo, 2021 Aiming at the problem that traditional convolutional neural network (CNN) continuously stacks convo-lutional layers and pooling layers in order to obtain high accuracy, resulting in complicated model structure, long training time, and single data ...
ZHANG Li, QIU Cunyue, ZHANG Kaixin, ZHANG Dabo, LUO Hao
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Convolutional neural networks: an overview and application in radiology
Insights into Imaging, 2018 Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology.
Rikiya Yamashita +3 more
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A robust deformed convolutional neural network (CNN) for image denoising
CAAI Transactions on Intelligence Technology, 2022 Abstract Due to strong learning ability, convolutional neural networks (CNNs) have been developed in image denoising. However, convolutional operations may change original distributions of noise in corrupted images, which may increase training difficulty in image denoising.
Qi Zhang 0059 +4 more
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An attention‐based cascade R‐CNN model for sternum fracture detection in X‐ray images
CAAI Transactions on Intelligence Technology, 2022 Fracture is one of the most common and unexpected traumas. If not treated in time, it may cause serious consequences such as joint stiffness, traumatic arthritis, and nerve injury.
Yang Jia +4 more
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Convolutional Neural Network (CNN) with Randomized Pooling
, 2022 Abstract Convolutional Neural Network (CNN) is a deep learning approach to solve complex problems, and it has been widely used in image processing for image classification, object identification, semantic segmentation etc. It has overcome the constraint of traditional machine learning approaches.
Hafiz Imran +2 more
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Application of Convolutional Neural Network (CNN) to Recognize Ship Structures
Sensors, 2022 The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN).
Jae-Jun Lim +6 more
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AAR-CNNs: Auto Adaptive Regularized Convolutional Neural Networks [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 In order to address the overfitting problem caused by the small or simple training datasets and the large model’s size in Convolutional Neural Networks (CNNs), a novel Auto Adaptive Regularization (AAR) method is proposed in this paper. The relevant networks can be called AAR-CNNs. AAR is the first method using the “abstraction extent” (predicted by AE
Yao Lu 0008 +3 more
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