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Entropy-Driven Intelligent Diagnosis for SMR Loss of Coolant Accidents: A CNN-LSTM-Attention Hybrid Model for Break Size Assessment. [PDF]

Entropy (Basel)
Yang L, Lei J.
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Image Processing and Deep Convolutional Neural Network Method for Automated Malaria Parasite Detection in Thin Blood Slide Images. [PDF]

Diagnostics (Basel)
Kumari K, Kaura T, Mewara A, Tewary S, Garg NM.   +4 more
europepmc   +1 more source

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A Fully Residual Convolutional Neural Network for Background Subtraction

Pattern Recognition Letters, 2021
Abstract Background subtraction is an important step involved in solving computer vision problems. This paper proposes a novel background subtraction method with fully residual convolutional neural network (FR-CNN). This fully residual connection helps to fuse the fine scale and coarse scale feature information efficiently.
Preeth Raguraman, R Mohan
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Fully shared convolutional neural networks

Neural Computing and Applications, 2021
Recently, the group convolutions are widely used in mobile convolutional neural networks (CNNs) to improve the model’s efficiency. However, the training process of these popular group-conv mobile models is usually time-consuming compared to the regular models.
Yao Lu 0008, Guangming Lu 0002, Jinxing Li 0003, Zheng Zhang 0006, Yuanrong Xu   +4 more
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Fully hardware-implemented memristor convolutional neural network

Nature, 2020
Memristor-enabled neuromorphic computing systems provide a fast and energy-efficient approach to training neural networks1-4. However, convolutional neural networks (CNNs)-one of the most important models for image recognition5-have not yet been fully hardware-implemented using memristor crossbars, which are cross-point arrays with a memristor device ...
Peng, Yao, Huaqiang, Wu, Bin, Gao, Jianshi, Tang, Qingtian, Zhang, Wenqiang, Zhang, J Joshua, Yang, He, Qian   +7 more
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Optimizing Fully Spectral Convolutional Neural Networks on FPGA

2020 International Conference on Field-Programmable Technology (ICFPT), 2020
Computing convolutional neural networks (CNNs) in frequency domain largely reduces the number of operations for training and inference of CNNs. However, existing designs with such an idea require repeated spatial- and frequency-domain switching due to the absence of nonlinear functions in the frequency domain, as such it makes the benefit less ...
Shuanglong Liu, Wayne Luk
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Crowd Counting with Fully Convolutional Neural Network

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
Crowd counting estimation is an extremely challenging task due to various crowded scenarios. In this paper, we present a deep learning framework for crowd counting from a single static image with different number of people and arbitrary perspective. In the design of convolutional neural network structure, we employ the VGG16 model but drop the fully ...
Ming Liu, Yang Liu, Jue Jiang, Zhenwei Guo, Zenan Wang   +4 more
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Scene text detection with fully convolutional neural networks

Multimedia Tools and Applications, 2019
Text detection in scene image has become a hot topic in computer vision and artificial intelligence research, due to its wide range of applications and challenges. Most state-of-the-art methods for text detection based on deep learning rely on text bounding box regression. These methods can not well handle the case that if the scene text is curved.
Zhandong Liu, Wengang Zhou 0001, Houqiang Li   +2 more
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Blind inpainting using the fully convolutional neural network

The Visual Computer, 2015
Most of existing inpainting techniques require to know beforehandwhere those damaged pixels are, i.e., non-blind inpainting methods. However, in many applications, such information may not be readily available. In this paper, we propose a novel blind inpainting method based on a fully convolutional neural network.
Nian Cai, Zhenghang Su, Zhineng Lin, Han Wang 0017, Zhijing Yang, Bingo Wing-Kuen Ling   +5 more
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Glioma Image Segmentation Method on Fully Convolutional Neural Network

Proceedings of the 6th International Conference on Biomedical Signal and Image Processing, 2021
Aiming at the difference in the segmentation performance of the three segmentation target regions in the glioma image segmentation task based on the fully convolutional neural network, we propose a comprehensive evaluation method of neural network performance based on four evaluation indices.
Lin Chen, Qihong Liu, Kai Liu, Jie Lu, Limin Song, Ke Yang   +5 more
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