Search Results - Very deep fully convolutional encoder–decoder network

Very deep fully convolutional encoder–decoder network based on wavelet transform for art image fusion in cloud computing environment

Authors: Tong Chen Juan Yang

Source: Evolving Systems. 14:281-293

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Very deep fully convolutional encoder–decoder network based on wavelet transform for art image fusion in cloud computing environment.

Authors: Chen, Tong Yang, Juan

Source: Evolving Systems; Apr2023, Vol. 14 Issue 2, p281-293, 13p

Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections

Authors: Mao, Xiao-Jiao Shen, Chunhua Yang, Yu-Bin

Index Terms: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/1603.09056

Attention-Guided Label Refinement Network for Semantic Segmentation of Very High Resolution Aerial Orthoimages

Authors: Jianfeng Huang Xinchang Zhang Ying Sun et al.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 4490-4503 (2021)

Subject Terms: Attention mechanism, convolutional neural networks (CNNs), deep learning, semantic segmentation, urban object extraction, very high spatial resolution images, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809

File Description: electronic resource

Relation: https://ieeexplore.ieee.org/document/9410460/; https://doaj.org/toc/2151-1535

Access URL: https://doaj.org/article/764513fc4ddb4b0182e1d8982e54c57c

A deep convolutional encoder-decoder architecture for autonomous fault detection of PV plants using multi-copters.

Authors: Moradi Sizkouhi, Amirmohammad Aghaei, Mohammadreza Esmailifar, Sayyed Majid

Source: Solar Energy. Jul2021, Vol. 223, p217-228. 12p.

Subject Terms: *SIGNAL convolution, *FEATURE extraction, *PIXELS, *DYNAMIC positioning systems

A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem.

Authors: Mohammadimanesh, Fariba Salehi, Bahram Mahdianpari, Masoud et al.

Source: ISPRS Journal of Photogrammetry & Remote Sensing. May2019, Vol. 151, p223-236. 14p.

Subject Terms: *LAND cover, *ARTIFICIAL neural networks, *SYNTHETIC aperture radar, *STRUCTURE-activity relationships, *WETLAND ecology, *SPECKLE interference, *ENGINEERING design

PolSAR image classification using shallow to deep feature fusion network with complex valued attention.

Authors: Alkhatib MQ Zitouni MS Al-Saad M et al.

Source: Scientific reports [Sci Rep] 2025 Jul 07; Vol. 15 (1), pp. 24315. Date of Electronic Publication: 2025 Jul 07.

Publication Type: Journal Article

Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE

Single infrared image enhancement using a deep convolutional neural network.

Authors: Kuang, Xiaodong Sui, Xiubao Liu, Yuan et al.

Source: Neurocomputing. Mar2019, Vol. 332, p119-128. 10p.

Subject Terms: *INFRARED imaging, *IMAGE intensifiers, *DEEP learning, *IMAGING system noise

Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic Segmentation

Authors: Nanfack, Geraldin Elhassouny, Azeddine Thami, Rachid Oulad Haj

Index Terms: Computer Science - Computer Vision and Pattern Recognition, text

URL: http://arxiv.org/abs/1711.05491

Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections

Authors: Mao, X. Shen, C. Yang, Y. et al.

Contributors: Mao, X. Shen, C. Yang, Y. et al.

Source: https://papers.nips.cc/paper/6172-image-restoration-using-very-deep-convolutional-encoder-decoder-networks-with-symmetric-skip-connections.

Relation: Advances in Neural Information Processing Systems; 29; http://purl.org/au-research/grants/arc/FT120100969; Advances in neural information processing systems, 2016 / Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (ed./s), vol.29, pp.2810-2818; http://hdl.handle.net/2440/124485; Shen, C. [0000-0002-8648-8718]

Availability: http://hdl.handle.net/2440/124485

Object segmentation in depth maps with one user click and a synthetically trained fully convolutional network

Authors: Grard, Matthieu Brégier, Romain Sella, Florian et al.

Index Terms: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Robotics, text

URL: http://arxiv.org/abs/1801.01281

Versatile Convolutional Networks Applied to Computed Tomography and Magnetic Resonance Image Segmentation.

Authors: Almeida, Gonçalo Tavares, João Manuel R. S.

Source: Journal of Medical Systems. Aug2021, Vol. 45 Issue 8, p1-10. 10p. 2 Color Photographs, 1 Black and White Photograph, 5 Diagrams, 4 Charts, 1 Graph.

Subject Terms: *DIGITAL image processing, *DEEP learning, *COMPUTERS in medicine, *MAGNETIC resonance imaging, *DIAGNOSTIC imaging, *COMPUTED tomography, *ARTIFICIAL neural networks, *THREE-dimensional printing

Automatic Polyp Recognition in Colonoscopy Images Using Deep Learning and Two-Stage Pyramidal Feature Prediction.

Authors: Jia, Xiao Mai, Xiaochun Cui, Yi et al.

Source: IEEE Transactions on Automation Science & Engineering; Jul2020, Vol. 17 Issue 3, p1570-1584, 15p

Subject Terms: CONVOLUTIONAL neural networks, IMAGE recognition (Computer vision), COMPUTER architecture, EARLY detection of cancer, IMAGE segmentation, ADENOMATOUS polyps, DEEP learning

Exploring deep fully convolutional neural networks for surface defect detection in complex geometries.

Authors: García Peña, Daniel García Pérez, Diego Díaz Blanco, Ignacio et al.

Source: International Journal of Advanced Manufacturing Technology; Sep2024, Vol. 134 Issue 1/2, p97-111, 15p

Subject Terms: MACHINE learning, CONVOLUTIONAL neural networks, THREE-dimensional imaging, IMAGE segmentation, METAL defects, DEEP learning

Combining Deep Fully Convolutional Network and Graph Convolutional Neural Network for the Extraction of Buildings from Aerial Images.

Authors: Zhang, Wenzhuo Yu, Mingyang Chen, Xiaoxian et al.

Source: Buildings (2075-5309); Dec2022, Vol. 12 Issue 12, p2233, 21p

Subject Terms: CONVOLUTIONAL neural networks, DEEP learning, REMOTE sensing, SPATIAL resolution, BUILDING-integrated photovoltaic systems

Company/Entity: WUHAN University

Concurrent Learning Approach for Estimation of Pelvic Tilt from Anterior–Posterior Radiograph.

Authors: Jodeiri, Ata Seyedarabi, Hadi Danishvar, Sebelan et al.

Source: Bioengineering (Basel); Feb2024, Vol. 11 Issue 2, p194, 13p

Subject Terms: TOTAL hip replacement, DEEP learning, RADIOGRAPHS, CONVOLUTIONAL neural networks, SURGICAL complications, IMAGE segmentation

Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images.

Authors: Chen, Guanzhou Zhang, Xiaodong Wang, Qing et al.

Source: IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing; May2018, Vol. 11 Issue 5, p1633-1644, 12p

A novel modular deep fully convolutional network for efficient low resolution facial expression recognition.

Authors: Aly, Walaa Shahin, Ahmed I. Aly, Saleh

Source: Journal of Ambient Intelligence & Humanized Computing; Jun2023, Vol. 14 Issue 6, p7747-7759, 13p

利用深度全卷积编解码网络的单通道语音增强. (Chinese)

Alternate Title: Single Channel Speech Enhancement Based on Deep Fully Convolutional Encoder-Decoder Neural Network. (English)

Authors: 时文华 张雄伟 邹霞 et al.

Source: Journal of Signal Processing; Apr2019, Vol. 35 Issue 4, p631-640, 10p

Lost data neural semantic recovery framework for structural health monitoring based on deep learning.

Authors: Jiang, Kejie Han, Qiang Du, Xiuli

Source: Computer-Aided Civil & Infrastructure Engineering; Jul2022, Vol. 37 Issue 9, p1160-1187, 28p

Subject Terms: DEEP learning, STRUCTURAL health monitoring, CONVOLUTIONAL neural networks, CONDITIONAL probability, DATA recovery, DISTRIBUTION (Probability theory)