| Records |
| Author |
Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla |
Title  |
Human Pose Estimation through A Novel Multi-View Scheme |
Type |
Conference Article |
| Year |
2022 |
Publication |
Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 |
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5 |
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855-862 |
| Keywords |
Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach |
| Abstract |
This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human
pose estimation problem. The proposed approach first obtains the human body joints of a set of images,
which are captured from different views at the same time. Then, it enhances the obtained joints by using a
multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from
another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed
for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and
comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements
in the accuracy of body joints estimations. |
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yes |
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cidis @ cidis @ |
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169 |
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| Author |
Jorge Charco Aguirre |
| Title |
Human Pose Estimation based in Deep Learning Techniques from Multi-view Environments |
Type |
Book Chapter |
| Year |
2023 |
Publication |
Ediciones FIEC-ESPOL |
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no |
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cidis @ cidis @ |
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263 |
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| Author |
Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, Henry O. Velesaca. |
| Title |
Human Body Pose Estimation in Multi-view Environments. |
Type |
Book Chapter |
| Year |
2022 |
Publication |
ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series |
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BOOK |
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224 |
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79-99 |
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no |
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cidis @ cidis @ |
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197 |
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| Author |
Patricia Suarez & Angel D. Sappa |
| Title |
Haze-Free Imaging through Haze-Aware Transformer Adaptations |
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Conference Article |
| Year |
2024 |
Publication |
Lecture Notes in Networks and Systems: 4th International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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Vol. 1116 LNNS |
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27-37 |
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23673370 |
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978-981976994-0 |
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no |
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cidis @ cidis @ |
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236 |
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| Author |
Henry Velesaca Lara, Patricia Suarez, Darío Carpio & Angel Sappa |
| Title |
Fruit Grading based on Deep Learning and Active Vision System |
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Conference Article |
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2024 |
Publication |
2nd International Conference of Applied Industrial Engineering: Intelligent Production Automation and its Sustainable Development, CIIA 2024 Guayaquil 28 – 30 May 2024 |
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Vol. 532 |
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25550403 |
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no |
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cidis @ cidis @ |
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241 |
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| Author |
Tommy David Beltran Borbor, Raul Josue Villao Rodriguez, Luis Enrique Chuquimarca Jiménez, Boris Xavier Vintimilla Burgos & Sergio Alejandro Velastin |
| Title |
Fruit Deformity Classification through Single-Input and Multi-Input Architectures based on CNN Models using Real and Synthetic Images |
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Conference Article |
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2025 |
Publication |
Lecture Notes in Computer Science: 27th The Iberomican Congress on Pattern Recognition CIARP 2024 Talca 26 – 29 Noviembre 2024 |
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Vol. 15368 LNCS |
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46-62 |
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03029743 |
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978-303176606-0 |
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cidis @ cidis @ |
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251 |
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| Author |
Luis Chuquimarca, Renzo Pacheco, Paula Gonzalez, Boris Vintimilla & Sergio Velastin |
| Title |
Fruit defect detection using CNN models with real and virtual data. |
Type |
Conference Article |
| Year |
2023 |
Publication |
18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) Lisbon 19-21 Febraury 2024 |
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Vol. 4 |
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272 - 279 |
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21845921 |
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no |
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cidis @ cidis @ |
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203 |
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| Author |
Sebastián Fuenzalida; Keyla Toapanta; Jonathan S. Paillacho Corredores; Dennys Paillacho |
| Title |
Forward and Inverse Kinematics of a Humanoid Robot Head for Social Human Robot-Interaction |
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Conference Article |
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2019 |
Publication |
IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador |
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This paper presents an analysis of forward and inverse kinematics for a humanoid robotic head. The robotic head is used for the study of social human-robot interaction, such as a support tool to maintain the attention of patients with Autism Spectrum Disorder. The design of a parallel robot that emulates human head movements through a closed structure is presented. The position and orientation in this space is controlled by three servomotors. For this, the solutions made for the kinematic problem are encompassed by a geometric analysis of a mobile base. This article describes a non-systematic method,
called the geometric method, and compares some of the most popular existing methods considering reliability and computational cost. The geometric method avoids the use of changing reference systems, and instead uses geometric
relationships to directly obtain the position based on joint variables; and the other way around. Therefore, it converges in a few iterations and has a low computational cost. |
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gtsi @ user @ |
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113 |
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| Author |
Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |
| Title |
Fine-tuning deep convolutional networks for lepidopterous genus recognition |
Type |
Journal Article |
| Year |
2017 |
Publication |
Lecture Notes in Computer Science |
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Vol. 10125 LNCS |
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pp. 467-475 |
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gtsi @ user @ |
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63 |
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| Author |
Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |
| Title |
Fine-tuning based deep covolutional networks for lepidopterous genus recognition |
Type |
Conference Article |
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2016 |
Publication |
XXI IberoAmerican Congress on Pattern Recognition |
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1-9 |
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This paper describes an image classi cation approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio |
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cidis @ cidis @ |
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