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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 | Abbreviated Journal | |
| Volume | Issue | Pages | 4354-4362 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial  |
151 | ||
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| Author | Jacome-Galarza L.-R | ||||
| Title | Crop yield prediction utilizing multimodal deep learning | Type | Conference Article | ||
| Year | 2021 | Publication | 16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada. | ||||
| Abstract | La agricultura de precisión es una práctica vital para mejorar la producción de cosechas. El presente trabajo tiene como objetivo desarrollar un modelo multimodal de aprendizaje profundo que es capaz de producir un mapa de salud de cosechas. El modelo recibe como entradas imágenes multiespectrales y datos de sensores de campo (humedad, temperatura, estado del suelo, etc.) y crea un mapa de rendimiento de la cosecha. La utilización de datos multimodales tiene como finalidad extraer patrones ocultos del estado de salud de las cosechas y de esta manera obtener mejores resultados que los obtenidos mediante los índices de vegetación. |
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| Language | Español | Summary Language | Original Title | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial |
150 | ||
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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 | Abbreviated Journal | |
| Volume | Issue | Pages | 7196-7202 | ||
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| Call Number | cidis @ cidis @ | Serial |
149 | ||
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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 | Abbreviated Journal | |
| Volume | Issue | Pages | 2703-2712 | ||
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| Call Number | cidis @ cidis @ | Serial |
148 | ||
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| Author | Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. | ||||
| Title | Camera pose estimation in multi-view environments:from virtual scenarios to the real world | Type | Journal Article | ||
| Year | 2021 | Publication | In Image and Vision Computing Journal. (Article number 104182) | Abbreviated Journal | |
| Volume | Vol. 110 | Issue | Pages | ||
| Keywords | Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments | ||||
| Abstract | This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios. |
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| Language | English | Summary Language | English | Original Title | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial |
147 | ||
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| Author | Rosero Vasquez Shendry | ||||
| Title | Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020. | Type | Journal Article | ||
| Year | 2020 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 615-625 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial |
145 | ||
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| Author | Patricia L. Suarez | ||||
| Title | Procesamiento y representación de imágenes multiespectrales usando técnicas de aprendizaje profundo (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). Ph.D. thesis. | Type | Book Chapter | ||
| Year | 2020 | Publication | Ediciones FIEC-ESPOL. | Abbreviated Journal | |
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| Corporate Author | Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector. | Thesis | |||
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| Language | Español | Summary Language | Original Title | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial |
144 | ||
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| Author | Morocho-Cayamcela, M.E. | ||||
| Title | Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling | Type | Journal Article | ||
| Year | 2020 | Publication | Electronic Letters on Computer Vision and Image Analysis (ELCVIA) | Abbreviated Journal | |
| Volume | Vol. 19 | Issue | Issue 2 | Pages | pp. 17-21 |
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| Call Number | cidis @ cidis @ | Serial |
140 | ||
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| Author | Morocho-Cayamcela, M.E. & W. Lim | ||||
| Title | Lateral confinement of high-impedance surface-waves through reinforcement learning | Type | Journal Article | ||
| Year | 2020 | Publication | Electronics Letters | Abbreviated Journal | |
| Volume | Vol. 56 | Issue | 23, 12 November 2020 | Pages | pp. 1262-1264 |
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| Abstract | The authors present a model-free policy-based reinforcement learning model that introduces perturbations on the pattern of a metasurface. The objective is to learn a policy that changes the size of the patches, and therefore the impedance in the sides of an artificially structured material. The proposed iterative model assigns the highest reward when the patch sizes allow the transmission along a constrained path and penalties when the patch sizes make the surface wave radiate to the sides of the metamaterial. After convergence, the proposed model learns an optimal patch pattern that achieves lateral confinement along the metasurface. Simulation results show that the proposed learned-pattern can effectively guide the electromagnetic wave through a metasurface, maintaining its instantaneous eigenstate when the homogeneity is perturbed. Moreover, the pattern learned to prevent reflections by changing the patch sizes adiabatically. The reflection coefficient S1, 2 shows that most of the power gets transferred from the source to the destination with the proposed design. |
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| Call Number | cidis @ cidis @ | Serial |
139 | ||
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| Author | Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla | ||||
| Title | Deep learning-based vegetation index estimation | Type | Book Chapter | ||
| Year | 2021 | Publication | Generative Adversarial Networks for Image-to-Image Translation Book. | Abbreviated Journal | |
| Volume | Chapter 9 | Issue | Issue 2 | Pages | 205-232 |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial |
137 | ||
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