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2020 Index IEEE Transactions on Image Processing Vol. 29

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i> &nbsp;
., Ensemble of Deep Convolutional Neural Networks With Gabor Face Representations for Face Recognition.  ...  ., +, TIP 2020 4556-4567 Deep Recognition of Vanishing-Point-Constrained Building Planes in Urban Street Views.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2020.3046056">doi:10.1109/tip.2020.3046056</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/24m6k2elprf2nfmucbjzhvzk3m">fatcat:24m6k2elprf2nfmucbjzhvzk3m</a> </span>
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Place recognition: An Overview of Vision Perspective [article]

Zhiqiang Zeng, Jian Zhang, Xiaodong Wang, Yuming Chen, Chaoyang Zhu
<span title="2017-06-17">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Thus we then introduce recent progress of visual place recognition system based on CNNs to automatically learn better image representations for places.  ...  Recently, the rapid advances of related fields such as object detection and image classification have inspired a new technique to improve visual place recognition system, i.e., convolutional neural networks  ...  CONVOLUTIONAL NEURAL NETWORKS Recently Convolutional Neural Networks achieve state-of-the-art performance on various classification and recognition tasks, e.g., handwriting digits recognition, object classification  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1707.03470v1">arXiv:1707.03470v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sy6d3ja7irbevjcq5airn2bvxy">fatcat:sy6d3ja7irbevjcq5airn2bvxy</a> </span>
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Functional Intelligence-Based Scene Recognition Scheme for MAV Environment-Adaptive Navigation

Lingling Wang, Yixin Liu, Li Fu, Yaning Wang, Ning Tang
<span title="2022-05-07">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ycpqunwwuja6beynxtnfswzqgy" style="color: black;">Drones</a> </i> &nbsp;
of two deep learning frameworks, SceneNet and Mobile Net-V2 was proposed to extract scene features for accurate diverse scenes recognition.  ...  Then, based on sufficient environment-sensitive measurements from the environment perception subsystem in FIAN, the two-level scene recognition method (TSRM) in the decision-making subsystem consisting  ...  Ding [21] came up with a deep neural network coined the noise-resistant network. Zhang [22] proposed an asymmetric encoder-decoder structure based on ResNet.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/drones6050120">doi:10.3390/drones6050120</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ay7hkoqpybdofej3mbw3d3llim">fatcat:ay7hkoqpybdofej3mbw3d3llim</a> </span>
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WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning

Mohamed R. Ibrahim, James Haworth, Tao Cheng
<span title="2019-12-02">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fw2uac7a5jggbfgqwh2wqy7yxu" style="color: black;">ISPRS International Journal of Geo-Information</a> </i> &nbsp;
A pipeline of four deep convolutional neural network (CNN) models, so-called WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions  ...  Despite the significance of this subject, it has still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual  ...  the Convolutional Neural Network models for the test sets.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijgi8120549">doi:10.3390/ijgi8120549</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qrycgtqfy5ebdd7tig55bhmyga">fatcat:qrycgtqfy5ebdd7tig55bhmyga</a> </span>
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Table of contents

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i> &nbsp;
Zhu 3254 Ensemble of Deep Convolutional Neural Networks With Gabor Face Representations for Face Recognition .......... .................................................................................  ...  Hua 5336 Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC ....................... ..............................................................................  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tip.2019.2940372">doi:10.1109/tip.2019.2940372</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h23ul2rqazbstcho46uv3lunku">fatcat:h23ul2rqazbstcho46uv3lunku</a> </span>
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2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2n2tpw5ang73osulebz6bm4ju" style="color: black;">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</a> </i> &nbsp;
Classification of High-Spatial-Resolution Remote Sensing Scenes Method Using Transfer Learning and Deep Convolutional Neural Network.  ...  ., +, JSTARS 2020 2853-2860 A Deep Neural Network Combined CNN and GCN for Remote Sensing Scene Classification.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jstars.2021.3050695">doi:10.1109/jstars.2021.3050695</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ycd5qt66xrgqfewcr6ygsqcl2y">fatcat:ycd5qt66xrgqfewcr6ygsqcl2y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210130035747/https://ieeexplore.ieee.org/ielx7/4609443/8994817/09335063.pdf?tp=&amp;arnumber=9335063&amp;isnumber=8994817&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/06/c7/06c7a3b3f370cd0b48d913515626cba65fde5abf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jstars.2021.3050695"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Neural Rerendering in the Wild

Moustafa Meshry, Dan B. Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, Ricardo Martin-Brualla
<span title="">2019</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ilwxppn4d5hizekyd3ndvy2mii" style="color: black;">2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)</a> </i> &nbsp;
For each photo, we render the scene points into a deep framebuffer, and train a neural network to learn the mapping of these initial renderings to the actual photos.  ...  We also compare results with prior work on scene reconstruction from internet photos.  ...  Neural scene rendering [6] applies deep neural networks to learn a latent scene representation that allows generation of novel views, but is limited to simple synthetic geometry.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2019.00704">doi:10.1109/cvpr.2019.00704</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cvpr/MeshryGKHPSM19.html">dblp:conf/cvpr/MeshryGKHPSM19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oim43sxz2fezlicvdptrfmn4ay">fatcat:oim43sxz2fezlicvdptrfmn4ay</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190711110701/http://openaccess.thecvf.com:80/content_CVPR_2019/papers/Meshry_Neural_Rerendering_in_the_Wild_CVPR_2019_paper.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c8/cc/c8ccefc4e60dea2755cd23f709fc684395391980.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cvpr.2019.00704"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning [article]

Mohamed R. Ibrahim, James Haworth, Tao Cheng
<span title="2019-10-22">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A pipeline of four deep Convolutional Neural Network (CNN) models, so-called the WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual  ...  Despite the significance of this subject, it is still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual  ...  ACKNOWLEDGMENT This research outcome is a part of a PhD study for the first author at University College London.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.09910v1">arXiv:1910.09910v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f27qxp4pfzatzbvui3hnfmberm">fatcat:f27qxp4pfzatzbvui3hnfmberm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200928054057/https://arxiv.org/ftp/arxiv/papers/1910/1910.09910.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/81/21/812136eba9e2489076e90b25089c7cce5d177a49.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.09910v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Deep Sensing of Urban Waterlogging [article]

Shi-Wei Lo, Jyh-Horng Wu, Jo-Yu Chang, Chien-Hao Tseng, Meng-Wei Lin, Fang-Pang Lin
<span title="2021-08-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The use of a deep sensing system in the monsoon season in Taiwan was demonstrated, and waterlogging events were predicted on the island-wide scale.  ...  Therefore, in this study, a visual sensing approach driven by deep neural networks and information and communication technology was developed to provide an end-to-end mechanism to realize waterlogging  ...  ACKNOWLEDGMENT We are indebted to Water Resources Agency (WRA) and Directorate General of Highways (DGH) in Taiwan for their video streaming.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.05927v3">arXiv:2103.05927v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7y7l2lxnxjez7jtozitvdxxlpu">fatcat:7y7l2lxnxjez7jtozitvdxxlpu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210819172120/https://arxiv.org/ftp/arxiv/papers/2103/2103.05927.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/79/e8/79e826def133a5f8536543c0918e3a983a3e1259.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.05927v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving

Zhiyang Guo, Yingping Huang, Xing Hu, Hongjian Wei, Baigan Zhao
<span title="2021-02-15">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikdpfme5h5egvnwtvvtjrnntyy" style="color: black;">Electronics</a> </i> &nbsp;
With the rise of the convolutional neural network (CNN)-based deep learning technique, research on scene understanding has achieved significant progress.  ...  This paper aims to provide a comprehensive survey of deep learning-based approaches for scene understanding in autonomous driving.  ...  The deep-learning based approaches for scene text detection and recognition for the purpose of scene understanding were reviewed in [3] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10040471">doi:10.3390/electronics10040471</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gyloykg24nbqvlw4ujiiagoneq">fatcat:gyloykg24nbqvlw4ujiiagoneq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429011436/https://res.mdpi.com/d_attachment/electronics/electronics-10-00471/article_deploy/electronics-10-00471.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f1/6c/f16c7a95b3797dbfd1d8d5b48c424f3d1f862257.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/electronics10040471"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Proposing an Image Enhancement Algorithm Using CNN for Applications of Face Recognition System

Phat Nguyen Huu, Loc Hoang Bao, Hoang Lai The
<span title="2020-01-17">2020</span> <i title="Sciencedomain International"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/k56mnurwuzag3fbhvbcfx2rdmm" style="color: black;">Journal of Advances in Mathematics and Computer Science</a> </i> &nbsp;
Neural Network (CNN) based on the available datasets and our self-built data.  ...  In recent years, we have witnessed tremendous improvements in face recognition performance from complex deep neural network architectures trained on millions of face images.  ...  In [4] [5] [6] more constraints are obtained from multiple images of the same scene under different weather conditions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.9734/jamcs/2019/v34i630230">doi:10.9734/jamcs/2019/v34i630230</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/67iwi4vn5zajjajudps3bedvya">fatcat:67iwi4vn5zajjajudps3bedvya</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200507151126/http://www.journaljamcs.com/index.php/JAMCS/article/download/30230/56715" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f3/42/f3420757b8086bb066ffa933b00d9d99ab067cda.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.9734/jamcs/2019/v34i630230"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Anomaly Detection in Video Sequences: A Benchmark and Computational Model [article]

Boyang Wan and Wenhui Jiang and Yuming Fang and Zhiyuan Luo and Guanqun Ding
<span title="2021-06-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Leveraging the above benefits from the LAD database, we further formulate anomaly detection as a fully-supervised learning problem and propose a multi-task deep neural network to solve it.  ...  Experimental results show that the proposed method outperforms the state-of-the-art anomaly detection methods on our database and other public databases of anomaly detection.  ...  Similarly, Xu et al. proposed a deep neural clustering extracted normal trajectories of moving objects in video network based model by constructing a stacked denoising autoen- sequences [21, 23].  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.08570v1">arXiv:2106.08570v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oeju32uh65fpliiewsfmhupu44">fatcat:oeju32uh65fpliiewsfmhupu44</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210618134901/https://arxiv.org/pdf/2106.08570v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d3/d5/d3d5836cfd6de0441f3b84cc8b5afc3407739c76.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.08570v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Deep Sensing of Urban Waterlogging

Shi-Wei Lo, Jyh-Horng Wu, Jo-Yu Chang, Chien-Hao Tseng, Meng-Wei Lin, Fang-Pang Lin
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Deep neural network, internet of video things, urban flood, urban waterlogging, visual sensing.  ...  The use of a deep sensing system in the monsoon season in Taiwan was demonstrated, and waterlogging events were predicted on the island-wide scale.  ...  ACKNOWLEDGMENT The authors are indebted to Water Resources Agency (WRA) and Directorate General of Highways (DGH) in Taiwan for their video streaming.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3111623">doi:10.1109/access.2021.3111623</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vrh3cwtwujcr5pkrsiccqk5sry">fatcat:vrh3cwtwujcr5pkrsiccqk5sry</a> </span>
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Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Fahad Jibrin Abdu, Yixiong Zhang, Maozhong Fu, Yuhan Li, Zhenmiao Deng
<span title="2021-03-10">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models.  ...  Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks.  ...  The fusion system consists of three stages: the radar-based object detection, the vision-based object recognition, and the fusion stage based on the radial basis function neural network (RBFNN) that runs  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717125638/https://res.mdpi.com/d_attachment/sensors/sensors-21-01951/article_deploy/sensors-21-01951-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/71/6e/716e22702105519ae4adfa7d4d96307044d3b4b9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s21061951"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7999239" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Predicting Geo-informative Attributes in Large-Scale Image Collections Using Convolutional Neural Networks

Stefan Lee, Haipeng Zhang, David J. Crandall
<span title="">2015</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wsjivbkuezdvxdnrhihbwjrxlu" style="color: black;">2015 IEEE Winter Conference on Applications of Computer Vision</a> </i> &nbsp;
We learn models for these attributes using a large (noisy) set of geo-tagged images from Flickr by training deep convolutional neural networks (CNNs).  ...  Most work in automatically estimating geo-tags from image content is based on comparison against models of buildings or landmarks, or on matching to large reference collections of geotagged images.  ...  We apply deep convolutional neural networks (CNNs) to this problem, and compare their performance to standard techniques including both scene-level (e.g.  ... 
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