| Records |
| Author |
Armin Mehri, Parichehr Behjati, Dario Carpio, and Angel D. Sappa |
| Title |
SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE access |
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| Volume |
Vol. 11 |
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Pages  |
121457 - 121469 |
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| ISSN |
21693536 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
227 |
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| Author |
Xavier Soria , Gonzalo Pomboza-Junez & Angel Sappa. |
| Title |
LDC: Lightweight Dense CNN for Edge Detection. |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Access journal |
Abbreviated Journal |
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| Volume |
Vol. 10 |
Issue |
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Pages |
pp. 68281-68290 |
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Approved |
yes |
| Call Number |
cidis @ cidis @ |
Serial |
183 |
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| Author |
Armin Mehri; Parichehr Behjati; Angel Domingo Sappa |
| Title |
TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. |
Type |
Journal Article |
| Year |
2023 |
Publication |
IEEE Access |
Abbreviated Journal |
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| Volume |
Vol. 11 |
Issue |
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Pages |
pp. 11529-11540 |
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21693536 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
207 |
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| Author |
Steven Silva, Nervo Verdezoto, Dennys Paillacho, Samuel Millan-Norman & Juan David Hernandez |
| Title |
Online Social Robot Navigation in Indoor, Large and Crowded Environments. |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Robotics and Automation (ICRA 2023) |
Abbreviated Journal |
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| Volume |
2023-May |
Issue |
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Pages |
9749 - 9756 |
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10504729 |
ISBN |
979-835032365-8 |
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Approved |
no |
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cidis @ cidis @ |
Serial |
206 |
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| Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
| Title |
LiNet: A Lightweight Network for Image Super Resolution |
Type |
Conference Article |
| Year |
2021 |
Publication |
25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 |
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Issue |
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Pages |
7196-7202 |
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Approved |
no |
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cidis @ cidis @ |
Serial |
149 |
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| Author |
Rivadeneira R.E., Sappa A.D., Vintimilla B.X., Nathan S., Kansal P., Mehri A et al. |
| Title |
Thermal Image Super-Resolution Challenge – PBVS 2021. |
Type |
Conference Article |
| Year |
2021 |
Publication |
In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 |
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Issue |
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Pages |
4354-4362 |
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Approved |
no |
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cidis @ cidis @ |
Serial |
151 |
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| Author |
Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa |
| Title |
Feature Point Descriptors: Infrared and Visible Spectra |
Type |
Journal Article |
| Year |
2014 |
Publication |
Sensors Journal |
Abbreviated Journal |
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| Volume |
Vol. 14 |
Issue |
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Pages |
pp. 3690-3701 |
| Keywords |
cross-spectral imaging; feature point descriptors |
| Abstract |
This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. |
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English |
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English |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
28 |
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| Author |
Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla |
| Title |
Cycle generative adversarial network: towards a low-cost vegetation index estimation |
Type |
Conference Article |
| Year |
2021 |
Publication |
IEEE International Conference on Image Processing (ICIP 2021) |
Abbreviated Journal |
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| Volume |
2021-September |
Issue |
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Pages |
2783-2787 |
| Keywords |
CyclicGAN, NDVI, near infrared spectra, instance normalization. |
| Abstract |
This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
164 |
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| Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
| Title |
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. |
Type |
Conference Article |
| Year |
2021 |
Publication |
In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 |
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Issue |
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Pages |
2703-2712 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
148 |
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| Author |
M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé |
| Title |
Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains |
Type |
Conference Article |
| Year |
2015 |
Publication |
Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 |
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Issue |
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Pages |
2488 - 2495 |
| Keywords |
Birds, Training, Legged locomotion, Visualization, Histograms, Object recognition, Gaussian mixture model |
| Abstract |
In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline. |
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| Publisher |
IEEE |
Place of Publication |
Hamburg, Germany |
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English |
Summary Language |
English |
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Conference |
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Notes |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
41 |
| Permanent link to this record |
