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Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks
2016
The European Symposium on Artificial Neural Networks
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Thermal cameras provide an additional input channel that helps solving this task and deep convolutional networks are the currently leading approach for many pattern recognition problems, including object ...
In this paper, we explore the potential of deep models for multispectral pedestrian detection. We investigate two deep fusion architectures and analyze their performance on multispectral data. ...
With the recent interest of the vision community in convolutional neural networks (CNNs), an increasing number of top performing detectors utilize CNNs. ...
dblp:conf/esann/WagnerFHB16
fatcat:pkgv4zu4lfejxij6wx4yb6apia
Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images
[article]
2022
arXiv
pre-print
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Hence, we performed a comprehensive study to prove our point that, enhancement of resolution by Super-Resolution Convolutional Neural Network (SRCNN) will lessen the chance of misclassification of pixels ...
For a precise quantification of forest land cover changes, the availability of spatially fine resolution data is a necessity. ...
Super Resolution Convolutional Neural Network SRCNN produces expanded images with improved details. ...
arXiv:2207.02301v1
fatcat:5iqspduklfgwzmkyyehbbynrka
Table of contents
2020
IEEE Geoscience and Remote Sensing Letters
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Hong 1752 Multispectral Data Multispectral Change Detection With Bilinear Convolutional Neural Networks ............................................. ................................................... ...
Shi 1782 Fusing Multiseasonal Sentinel-2 Imagery for Urban Land Cover Classification With Multibranch Residual Convolutional Neural Networks ............................................ C. Qiu, L. ...
doi:10.1109/lgrs.2020.3024496
fatcat:uurtkjc7unfbhgo4ppnopd47xu
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
[article]
2018
arXiv
pre-print
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Such illumination information is incorporated into two-stream deep convolutional neural networks to learn multispectral human-related features under different illumination conditions (daytime and nighttime ...
Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy. ...
deep convolutional neural networks pre-trained on the ImageNet dataset [32] in parallel. ...
arXiv:1802.09972v1
fatcat:5pr765baurfqthjc7oqdnkdg2q
A Comparison of Deep Saliency Map Generators on Multispectral Data in Object Detection
[article]
2021
arXiv
pre-print
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Deep neural networks, especially convolutional deep neural networks, are state-of-the-art methods to classify, segment or even generate images, movies, or sounds. ...
The dataset used in this work is the Multispectral Object Detection Dataset, where each scene is available in the FIR, MIR and NIR as well as visual spectrum. ...
Introduction Computer vision is no longer imaginable without convolutional deep neural networks. ...
arXiv:2108.11767v1
fatcat:gveqnlaivzdd5apox7bcaodrui
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
[article]
2019
arXiv
pre-print
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It offers two major advantages over the existing anchor box based multispectral detection methods. ...
In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared ...
neural networks for multispectral target detection. ...
arXiv:1902.05291v1
fatcat:h7qdwr6g3rfm7iqjyaaf5dduvy
Robust Facial Recognition System using One Shot Multispectral Filter Array Acquisition System
2022
International Journal of Advanced Computer Science and Applications
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For this goal, a MSFA one-shot camera was used to collect the images and a robust facial recognition method based on Fast Discrete Curvelet Transform and Convolutional Neural Network is proposed. ...
On the other hand, currently there are snapshot multispectral imaging systems which integrate a single sensor with Multispectral Filter Arrays (MSFA) allow having at each acquisition an image on several ...
The rate of 100% was reached with the deep convolutional neural network algorithm. In general systems using neural networks algorithm have a rate close to 100%. ...
doi:10.14569/ijacsa.2022.0130104
fatcat:xzuz4y7w4rflhdijhfxbok63yu
2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2020
4789-4802
Deep Depthwise Separable Convolutional Network for Change Detection in
Optical Aerial Images. ...
Xu, Y., +, JSTARS
2020 72-88
Deep Depthwise Separable Convolutional Network for Change Detection in
Optical Aerial Images. ...
A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020 ...
doi:10.1109/jstars.2021.3050695
fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review-Part I: Evolution and Recent Trends
2020
Remote Sensing
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To lower the barriers for researchers in EO, this review gives an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). ...
By changing the head structure after the convolutional backbone, the network can be changed to perform a completely different task, such as image segmentation object detection or instance segmentation. ...
] [33] Rectified Linear Unit CNN [2] Convolutional Neural Network RNN [34] Recurrent Neural Network LSTM [35] Long Short Term Memory GAN [36] Generative Adversarial Network IR Image Recognition ...
doi:10.3390/rs12101667
fatcat:vlqupucfrndexnyhrsgawshc2y
Table of contents
2020
IEEE Transactions on Geoscience and Remote Sensing
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Gao 7860 Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF) ................................................................. ...
Zhang
7815
High-Resolution Remote Sensing Image Scene Classification via Key Filter Bank Based on Convolutional Neural
Network ....................................................................... ...
doi:10.1109/tgrs.2020.3029642
fatcat:hpvuqstttzbapkewxrlfvfvb6m
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
[article]
2018
arXiv
pre-print
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First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. ...
Our extensive evaluation demonstrates that the proposed approach outperforms the state-of- the-art on the challenging KAIST multispectral pedestrian dataset and it is competitive with previous methods ...
, this is the first work specifically addressing the problem of pedestrian detection under adverse illumination conditions with convolutional neural networks. ...
arXiv:1704.02431v2
fatcat:keuvqg4dfzbu3e6h4eemrcj34e
Learning Cross-Modal Deep Representations for Robust Pedestrian Detection
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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First, given a multimodal dataset, a deep convolutional network is employed to learn a non-linear mapping, modeling the relations between RGB and thermal data. ...
Our extensive evaluation demonstrates that the proposed approach outperforms the state-ofthe-art on the challenging KAIST multispectral pedestrian dataset and it is competitive with previous methods on ...
under adverse illumination conditions with convolutional neural networks. ...
doi:10.1109/cvpr.2017.451
dblp:conf/cvpr/XuORWS17
fatcat:dx3gluuoffdklouxprura6zaty
Using Super-Resolution Algorithms for Small Satellite Imagery: A Systematic Review
2022
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Kinga Karwowska received the M.Sc. degree in geoinformatics in 2020 from the Military University of Technology, Warsaw, Poland, where she is currently working toward the Ph.D. degree with the Doctoral ...
Index Terms-Convolutional neural networks, deep learning, neural networks, single image super-resolution (SISR), superresolution.
I. ...
The reason is the problem with obtaining HR images that are necessary at the training stage of convolutional neural networks. ...
doi:10.1109/jstars.2022.3167646
fatcat:ghiywhrlx5bjninkzy3dxbmvoy
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
[article]
2019
arXiv
pre-print
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with the supervised multispectral pedestrian detectors. ...
The parameters of detector are updated using the generated labels by minimizing our defined multi-detection loss function with back-propagation. ...
detection supervision module with the box-level segmentation supervised deep neural networks [3] to build multispectral pedestrian detector, as illustrated in Fig. 3 . ...
arXiv:1904.03692v1
fatcat:2ehsx7bmdzc4jestsife7yfu6y
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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with the supervised multispectral pedestrian detectors. ...
The parameters of detector are updated using the generated labels by minimizing our defined multi-detection loss function with backpropagation. ...
detection supervision module with the box-level segmentation supervised deep neural networks [3] to build multispectral pedestrian detector, as illustrated in Fig. 3 . ...
doi:10.1109/cvprw.2019.00057
dblp:conf/cvpr/GuanLCYCVY19
fatcat:natgrocvpbhh3gzkv7b5xpqyyq
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