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Author |
Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa |
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Title |
Corn Kernel Classification From Few Training Samples |
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Journal Article |
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Year |
2023 |
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In journal Artificial Intelligence in Agriculture |
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Vol. 9 |
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pp. 89-99 |
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25897217 |
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cidis @ cidis @ |
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223 |
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Author |
Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa |
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Title |
Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach |
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Conference Article |
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2023 |
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17th International Conference On Signal Image Technology & Internet Based System |
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cidis @ cidis @ |
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224 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa |
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Title |
Boosting Guided Super-Resolution Performance with Synthesized Images |
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Conference Article |
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2023 |
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17th International Conference On Signal Image Technology & Internet Based Systems |
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cidis @ cidis @ |
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225 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa |
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Title |
Depth Map Estimation from a Single 2D Image |
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Conference Article |
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Year |
2023 |
Publication |
17th International Conference On Signal Image Technology & Internet Based Systems |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
226 |
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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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Year |
2023 |
Publication |
IEEE access |
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Volume |
Vol. 11 |
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Pages |
121457 - 121469 |
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21693536 |
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no |
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cidis @ cidis @ |
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227 |
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Author |
Sara Nieto, Evelyn Mejia, Ricardo Villacis, Fernanda Calderon, Hector Villegas, Jonathan Paillacho and Miguel Realpe |
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Title |
A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning |
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Conference Article |
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Year |
2023 |
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International Conference on Geospatial Information Sciences, iGISc 2023 |
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no |
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cidis @ cidis @ |
Serial |
228 |
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Author |
Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa |
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Title |
Tiny and Efficient Model for the Edge Detection Generalization |
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Conference Article |
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Year |
2023 |
Publication |
Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 |
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1356 - 1365 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
229 |
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Author |
Rubio Abel; Agila Wilton; González Leandro; Aviles Jonathan |
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Title |
A Numerical Model for the Transport of Reactants in Proton Exchange Fuel Cells |
Type |
Conference Article |
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Year |
2023 |
Publication |
12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 Oshawa 29 August – 1 September 2023 |
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273 - 278 |
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979-835033793-8 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
230 |
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Author |
Suarez Patricia; Carpio Dario; Sappa Angel D. |
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Title |
A Deep Learning Based Approach for Synthesizing Realistic Depth Maps |
Type |
Conference Article |
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Year |
2023 |
Publication |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 22nd International Conference on Image Analysis and Processing, ICIAP 2023 Udine 11 – 15 September 2023 |
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14234 LNCS |
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Pages |
369 - 380 |
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ISSN |
03029743 |
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978-303143152-4 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
231 |
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Author |
Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa |
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Title |
Fast CNN Stereo Depth Estimation through Embedded GPU Devices |
Type |
Journal Article |
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Year |
2020 |
Publication |
Sensors 2020 |
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Vol. 2020-June |
Issue |
11 |
Pages |
pp. 1-13 |
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Keywords |
stereo matching; deep learning; embedded GPU |
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Abstract |
Current CNN-based stereo depth estimation models can barely run under real-time
constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art
evaluations usually do not consider model optimization techniques, being that it is unknown what is
the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models
on three different embedded GPU devices, with and without optimization methods, presenting
performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth
estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture
for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically
augmenting the runtime speed of current models. In our experiments, we achieve real-time inference
speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier,
and Jetson Nano embedded devices. |
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English |
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English |
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14248220 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
132 |
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