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Author |
Armin Mehri, Parichehr Behjati, Dario Carpio, and Angel D. Sappa |
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Title |
SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution |
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Journal Article |
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2023 |
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IEEE access |
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Vol. 11 |
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121457 - 121469 |
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21693536 |
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no |
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Call Number |
cidis @ cidis @ |
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227 |
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Author |
Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez |
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Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. |
Type |
Journal Article |
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Year |
2020 |
Publication |
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Abbreviated Journal |
In Sensors |
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Vol. 2020-August |
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16 |
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pp. 1-23 |
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Keywords |
object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities |
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Abstract |
This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO)
deep neural networks for the outdoor advertisement panel detection problem by handling multiple
and combined variabilities in the scenes. Publicity panel detection in images oers important
advantages both in the real world as well as in the virtual one. For example, applications like Google
Street View can be used for Internet publicity and when detecting these ads panels in images, it could
be possible to replace the publicity appearing inside the panels by another from a funding company.
In our experiments, both SSD and YOLO detectors have produced acceptable results under variable
sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex
background and multiple panels in scenes. Due to the diculty of finding annotated images for the
considered problem, we created our own dataset for conducting the experiments. The major strength
of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable
when the publicity contained inside the panel is analyzed after detecting them. On the other side,
YOLO produced better panel localization results detecting a higher number of True Positive (TP)
panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models
with dierent types of semantic segmentation networks and using the same evaluation metrics is
also included. |
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English |
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14248220 |
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no |
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Call Number |
cidis @ cidis @ |
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133 |
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Author |
Armin Mehri; Parichehr Behjati; Angel Domingo Sappa |
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Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. |
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Journal Article |
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Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
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Vol. 11 |
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Pages |
pp. 11529-11540 |
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21693536 |
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no |
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Call Number |
cidis @ cidis @ |
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207 |
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Author |
Marjorie Chalen; Boris X. Vintimilla |
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Title |
Towards Action Prediction Applying Deep Learning |
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Journal Article |
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Year |
2019 |
Publication |
Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 |
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Pages |
pp. 1-3 |
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Keywords |
action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm. |
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Abstract |
Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task. |
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no |
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Call Number |
cidis @ cidis @ |
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129 |
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