| Records |
Author  |
Henry Velesaca Lara, Juan Antonio Holgado & José Miguel Gutiérrez |
| Title |
Optimizing Smart Factory Operations: A Methodological Approach to Industrial System Implementation based on OPC-UA |
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Conference Article |
| Year |
2024 |
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Accepted in CIIA – II International Conference of Applied Industrial Engineering |
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no |
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cidis @ cidis @ |
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242 |
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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 |
| Year |
2024 |
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Accepted in CIIA – II International Conference of Applied Industrial Engineering |
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no |
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cidis @ cidis @ |
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241 |
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| Author |
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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cidis @ cidis @ |
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238 |
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| Author |
Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla |
| Title |
Human Pose Estimation through A Novel Multi-View Scheme |
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Conference Article |
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2022 |
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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 |
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Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach |
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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 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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cidis @ cidis @ |
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197 |
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| Author |
Juca Aulestia M., Labanda Jaramillo M., Guaman Quinche J., Coronel Romero E., Chamba Eras L., & Roberto Jacome Galarza |
| Title |
Open innovation at university: a systematic literature review |
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Journal Article |
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2020 |
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Advances in Intelligent Systems and Computing |
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1159 AISC, 2020 |
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3-14 |
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no |
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cidis @ cidis @ |
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189 |
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| Author |
Low S., Inkawhich N., Nina O., Sappa A. and Blasch E. |
| Title |
Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. |
Type |
Conference Article |
| Year |
2022 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. |
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CONFERENCE |
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2022-June |
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417-425 |
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This paper details the results and main findings of the
second iteration of the Multi-modal Aerial View Object
Classification (MAVOC) challenge. This year’s MAVOC
challenge is the second iteration. The primary goal of
both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) input
modalities. Teams are encouraged/challenged to develop
multi-modal approaches that incorporate complementary
information from both domains. While the 2021 challenge
showed a proof of concept that both modalities could be
used together, the 2022 challenge focuses on the detailed
multi-modal models. Using the same UNIfied COincident
Optical and Radar for recognitioN (UNICORN) dataset and
competition format that was used in 2021. Specifically, the
challenge focuses on two techniques, (1) SAR classification
and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods
and describing their performance on our blind test set. Notably, all of the top ten teams outperform our baseline. For
SAR classification, the top team showed a 129% improvement over our baseline and an 8% average improvement
from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average
improvement over 2021. |
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no |
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cidis @ cidis @ |
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177 |
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| Author |
Luis C. Herrera, Leslie del R. Lima, Nayeth I. Solorzano, Jonathan S. Paillacho & Dennys Paillacho. |
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Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. |
Type |
Conference Article |
| Year |
2021 |
Publication |
The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communication in Computer and Information Science |
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1388 |
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164-178 |
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cidis @ cidis @ |
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191 |
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| Author |
Luis Chuquimarca, Boris Vintimilla & Sergio Velastin |
| Title |
Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets. |
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Conference Article |
| Year |
2023 |
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2023 |
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536 - 543 |
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no |
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cidis @ cidis @ |
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202 |
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| Author |
Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin |
| Title |
Classifying Healthy and Defective Fruits with a Siamese Architecture and CNN Models |
Type |
Conference Article |
| Year |
2024 |
Publication |
Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS) |
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cidis @ cidis @ |
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245 |
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