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Author |
Dennys Paillacho; Cecilio Angulo; Marta Díaz. |
Title |
An Exploratory Study of Group-Robot Social Interactions in a Cultural Center |
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Conference Article |
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2015 |
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IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, International Conference on, Hamburg, Germany, 2015 |
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This article describes an exploratory study of social human-robot interaction with the experimental robotic platform MASHI. The experiences were carried out in La B`obila Cultural Center in Barcelona, Spain to study the visitor preferences, characterize the groups and their spatial relationships in this open and unstructured environment. Results showed that visitors prefers to play and dialogue with the robot. Children have the highest interest in interacting with the robot, more than young and adult visitors. Most of the groups consisted of more than 3 visitors, however the size of the groups during interactions was continuously changed. In static situations, the observed spatial relationships denotes a social cohesion in the human-robot interactions. |
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gtsi @ user @ |
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Tyrone Rodríguez, Adriana Guilindro, Paolo Piedrahita & Miguel Realpe |
Title |
Towards Birds Conservation in Dry Forest Ecosystems through Audio Recognition via Deep Learning |
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Conference Article |
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2024 |
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In 9th International Congress on Information and Communication Technology ICICT 2024 |
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cidis @ cidis @ |
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239 |
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Velesaca, H.O., Suárez, P. L., Mira, R., & Sappa, A.D. |
Title |
Computer Vision based Food Grain Classification: a Comprehensive Survey |
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Journal Article |
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2021 |
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In Computers and Electronics in Agriculture Journal. (Article number 106287) |
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Vol. 187 |
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cidis @ cidis @ |
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159 |
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Suárez P. |
Title |
Processing and Representation of Multispectral Images Using Deep Learning Techniques |
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Magazine Article |
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2021 |
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In Electronic Letters on Computer Vision and Image Analysis |
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Vol. 19 |
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Issue 2 |
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pp. 5-8 |
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Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector |
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Master's thesis |
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Español |
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yes |
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cidis @ cidis @ |
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122 |
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Patricia Suarez & Angel D. Sappa |
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Haze-Free Imaging through Haze-Aware Transformer Adaptations |
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2024 |
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In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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no |
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cidis @ cidis @ |
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236 |
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Rafael Rivadeneira, Henry Velesaca & Angel Sappa |
Title |
Cross-Spectral Image Registration: a Comparative Study and a New Benchmark Dataset |
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Conference Article |
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2024 |
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In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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no |
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cidis @ cidis @ |
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237 |
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Henry Velesaca, Boris Vintimilla, Jorge Vulgarin, Coen Antens & Alberto Rubio Pérez |
Title |
Deep Learning-based Multimodal Sensing Framework for AntiSpoofing Systems |
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Journal Article |
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2024 |
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In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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no |
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cidis @ cidis @ |
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238 |
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Author |
Mehri, A, Ardakani, P.B., Sappa, A.D. |
Title |
MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. |
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Conference Article |
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2021 |
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In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 |
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2703-2712 |
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no |
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cidis @ cidis @ |
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148 |
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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. |
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Conference Article |
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2021 |
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In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021., junio 19 – 25, 2021 |
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4354-4362 |
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cidis @ cidis @ |
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151 |
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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 |
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Journal Article |
Year |
2021 |
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In Image and Vision Computing Journal. (Article number 104182) |
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Vol. 110 |
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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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no |
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cidis @ cidis @ |
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147 |
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