Suchergebnisse - Very deep fully convolutional encoder–decoder network

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  1. 1

    Very deep fully convolutional encoderdecoder network based on wavelet transform for art image fusion in cloud computing environment von Chen, Tong, Yang, Juan

    ISSN: 1868-6478, 1868-6486
    Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023
    Veröffentlicht in Evolving systems (01.04.2023)
    “… Therefore, we propose a very deep fully convolutional encoderdecoder network based on wavelet transform for art image fusion in the cloud computing environment …”
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  2. 2

    A deep convolutional encoder-decoder architecture for autonomous fault detection of PV plants using multi-copters von Moradi Sizkouhi, Amirmohammad, Aghaei, Mohammadreza, Esmailifar, Sayyed Majid

    ISSN: 0038-092X, 1471-1257
    Veröffentlicht: New York Elsevier Ltd 15.07.2021
    Veröffentlicht in Solar energy (15.07.2021)
    “… •Feature extraction and up-sampling to pixel level output through a developed encoder-decoder network …”
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  3. 3

    Deep Residual Encoder-Decoder Networks for Desert Seismic Noise Suppression von Ma, Haitao, Yao, Haiyang, Li, Yue, Wang, Hongzhou

    ISSN: 1545-598X, 1558-0571
    Veröffentlicht: Piscataway IEEE 01.03.2020
    Veröffentlicht in IEEE geoscience and remote sensing letters (01.03.2020)
    “… In this letter, aiming at the intense interference of seismic exploration noise in the desert of China, a desert seismic noise reduction system based on deep residual encoder-decoder network is proposed …”
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  4. 4

    A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem von Mohammadimanesh, Fariba, Salehi, Bahram, Mahdianpari, Masoud, Gill, Eric, Molinier, Matthieu

    ISSN: 0924-2716, 1872-8235
    Veröffentlicht: Elsevier B.V 01.05.2019
    Veröffentlicht in ISPRS journal of photogrammetry and remote sensing (01.05.2019)
    “… [Display omitted] Despite the application of state-of-the-art fully Convolutional Neural Networks (CNNs …”
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  5. 5

    Single infrared image enhancement using a deep convolutional neural network von Kuang, Xiaodong, Sui, Xiubao, Liu, Yuan, Chen, Qian, Gu, Guohua

    ISSN: 0925-2312, 1872-8286
    Veröffentlicht: Elsevier B.V 07.03.2019
    Veröffentlicht in Neurocomputing (Amsterdam) (07.03.2019)
    “… In this paper, we propose a deep learning method for single infrared image enhancement. A fully convolutional neural network (CNN …”
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  6. 6

    Efficient segmentation and classification of the tumor using improved encoder-decoder architecture in brain MRI images von Ingle, Archana, Roja, Mani, Sankhe, Manoj, Patkar, Deepak

    ISSN: 1847-6996, 1847-7003
    Veröffentlicht: 10.11.2022
    “… precision and accuracy is indeed a time-consuming and very challenging task. So newer digital methods like deep learning algorithms are used for tumor diagnosis which may lead to far better results …”
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    A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-View Stereo Reconstruction From an Open Aerial Dataset von Liu, Jin, Ji, Shunping

    ISSN: 1063-6919
    Veröffentlicht: IEEE 01.06.2020
    “… However, these efforts were focused on close-range objects and only a very few of the deep learning-based methods were specifically designed for large-scale 3D urban reconstruction due to the lack …”
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    P-LINKNET: LINKNET WITH SPATIAL PYRAMID POOLING FOR HIGH-RESOLUTION SATELLITE IMAGERY von Ding, Y., Wu, M., Xu, Y., Duan, S.

    ISSN: 2194-9034, 1682-1750, 2194-9034
    Veröffentlicht: Gottingen Copernicus GmbH 21.08.2020
    “… Automatic extraction of buildings from high-resolution remote sensing imagery is very useful in many applications such as city management, mapping, urban planning and geographic information updating …”
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  9. 9

    Recurrent Encoder-Decoder Networks for Time-Varying Dense Prediction von Tao Zeng, Bian Wu, Jiayu Zhou, Davidson, Ian, Shuiwang Ji

    ISSN: 2374-8486
    Veröffentlicht: IEEE 01.11.2017
    “… Here, we proposed a general encoder-decoder network architecture that aims to addressing time-varying dense prediction problems …”
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    Delineation of agricultural fields in smallholder farms from satellite images using fully convolutional networks and combinatorial grouping von Persello, C., Tolpekin, V.A., Bergado, J.R., de By, R.A.

    ISSN: 0034-4257, 1879-0704
    Veröffentlicht: New York Elsevier Inc 15.09.2019
    Veröffentlicht in Remote sensing of environment (15.09.2019)
    “… Very High Resolution (VHR) satellite images can capture such information. However, the automated delineation of fields in smallholder farms is a challenging task …”
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    Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections von Xiao-Jiao, Mao, Shen, Chunhua, Yu-Bin, Yang

    ISSN: 2331-8422
    Veröffentlicht: Ithaca Cornell University Library, arXiv.org 01.09.2016
    Veröffentlicht in arXiv.org (01.09.2016)
    “… In this paper, we propose a very deep fully convolutional encoding-decoding framework for image restoration such as denoising and super-resolution …”
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    Hrlinknet: Linknet with High-Resolution Representation for High-Resolution Satellite Imagery von Wu, Muyu, Shu, Zhen, Zhang, Jinming, Hu, Xiangyun

    ISSN: 2153-7003
    Veröffentlicht: IEEE 11.07.2021
    “… Automatic extraction of buildings from high-resolution remote sensing imagery is very useful in many applications such as city management, mapping, urban planning and geographic information updating …”
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    Deep smoke segmentation von Yuan, Feiniu, Zhang, Lin, Xia, Xue, Wan, Boyang, Huang, Qinghua, Li, Xuelong

    ISSN: 0925-2312, 1872-8286
    Veröffentlicht: Elsevier B.V 10.09.2019
    Veröffentlicht in Neurocomputing (Amsterdam) (10.09.2019)
    “… Inspired by the recent success of fully convolutional networks (FCN) in semantic segmentation, we propose a deep smoke segmentation network to infer high quality segmentation masks from blurry smoke images …”
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    Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans von B.N., Anoop, Li, Karl, Honnorat, Nicolas, Rashid, Tanweer, Wang, Di, Li, Jinqi, Fadaee, Elyas, Charisis, Sokratis, Walker, Jamie M., Richardson, Timothy E., Wolk, David A., Fox, Peter T., Cavazos, José E., Seshadri, Sudha, Wisse, Laura E.M., Habes, Mohamad

    ISSN: 0165-0270, 1872-678X, 1872-678X
    Veröffentlicht: Netherlands Elsevier B.V 01.03.2025
    Veröffentlicht in Journal of neuroscience methods (01.03.2025)
    “… In this study, we explore the use of fully automated methods relying on state-of-the-art Deep Learning approaches to produce these annotations …”
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    Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles von Shahian Jahromi, Babak, Tulabandhula, Theja, Cetin, Sabri

    ISSN: 1424-8220, 1424-8220
    Veröffentlicht: Basel MDPI AG 09.10.2019
    Veröffentlicht in Sensors (Basel, Switzerland) (09.10.2019)
    “… Some fusion architectures can perform very well in lab conditions using powerful computational resources …”
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    Fine-Grained Building Change Detection From Very High-Spatial-Resolution Remote Sensing Images Based on Deep Multitask Learning von Sun, Ying, Zhang, Xinchang, Huang, Jianfeng, Wang, Haiying, Xin, Qinchuan

    ISSN: 1545-598X, 1558-0571
    Veröffentlicht: Piscataway IEEE 2022
    Veröffentlicht in IEEE geoscience and remote sensing letters (2022)
    “… Recently, fully convolutional neural networks (FCNs) have been proven to be capable of feature extraction and semantic segmentation of VHR images, but its ability in change detection is untested and unknown …”
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  17. 17

    Unsupervised Spectral-Spatial Feature Learning via Deep Residual Conv-Deconv Network for Hyperspectral Image Classification von Lichao Mou, Ghamisi, Pedram, Xiao Xiang Zhu

    ISSN: 0196-2892, 1558-0644
    Veröffentlicht: New York IEEE 01.01.2018
    Veröffentlicht in IEEE transactions on geoscience and remote sensing (01.01.2018)
    “… Specifically, our network is based on the so-called encoder-decoder paradigm, i.e., the input 3-D hyperspectral patch is first transformed into a typically lower dimensional space via a convolutional subnetwork (encoder …”
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    Encoder-Decoder based CNN and Fully Connected CRFs for Remote Sensed Image Segmentation von Gurumurthy, Vikas Agaradahalli

    ISSN: 2331-8422
    Veröffentlicht: Ithaca Cornell University Library, arXiv.org 14.10.2019
    Veröffentlicht in arXiv.org (14.10.2019)
    “… In this work, a deep Convolutional Neural Network (CNN) based on symmetric encoder-decoder architecture with skip connections is employed for the 2D semantic segmentation …”
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    A Novel Recurrent Encoder-Decoder Structure for Large-Scale Multi-view Stereo Reconstruction from An Open Aerial Dataset von Liu, Jin, Ji, Shunping

    ISSN: 2331-8422
    Veröffentlicht: Ithaca Cornell University Library, arXiv.org 16.03.2020
    Veröffentlicht in arXiv.org (16.03.2020)
    “… However, these efforts were focused on close-range objects and only a very few of the deep learning-based methods were specifically designed for large-scale 3D urban reconstruction due to the lack …”
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    Versatile Convolutional Networks Applied to Computed Tomography and Magnetic Resonance Image Segmentation von Almeida, Gonçalo, Tavares, João Manuel R. S.

    ISSN: 0148-5598, 1573-689X, 1573-689X
    Veröffentlicht: New York Springer US 01.08.2021
    Veröffentlicht in Journal of medical systems (01.08.2021)
    “… : computed tomography and magnetic resonance. The developed model is fully convolutional with an encoder-decoder structure and high-resolution pathways which can process …”
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