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Leo Thomas Ramos & Angel D. Sappa |
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Title |
Dual-branch ConvNeXt-based Network with Attentional Fusion Decoding for Land Cover Classification Using Multispectral Imagery |
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2025 |
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IEEE SoutheastCon 2025 |
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cidis @ cidis @ |
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271 |
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Author |
Leo Thomas Ramos & Angel D. Sappa |
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Title |
Enhanced Aerial Scene Classification Through ConvNeXt Architectures and Channel Attention |
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2025 |
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10th International Congress on Information and Communication Technology |
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cidis @ cidis @ |
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272 |
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Author |
Low S., Inkawhich N., Nina O., Sappa A. and Blasch E. |
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Title |
Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022. |
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Conference Article |
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Year |
2022 |
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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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Abstract |
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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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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Title |
Metrics Design of Usability and Behavior Analysis of a Human-Robot-Game Platform. |
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Conference Article |
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2021 |
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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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Call Number |
cidis @ cidis @ |
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191 |
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Author |
Luis Chuquimarca, Boris Vintimilla & Sergio Velastin |
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Title |
Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets. |
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Conference Article |
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2023 |
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Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) Lisbon, 19-21 Febrero 2023 |
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Vol. 5 |
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536 - 543 |
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21845921 |
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no |
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Call Number |
cidis @ cidis @ |
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202 |
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Author |
Luis Chuquimarca, Boris Vintimilla & Sergio Velastin |
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Title |
A Review of External Quality Inspection for Fruit Grading using CNN Models |
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2024 |
Publication |
Artificial Intelligence in Agriculture |
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Vol. 14 |
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1-20 |
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25897217 |
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no |
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cidis @ cidis @ |
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254 |
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Author |
Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin |
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Title |
Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models |
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Conference Article |
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2024 |
Publication |
14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024 |
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979-835037565-7 |
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Call Number |
cidis @ cidis @ |
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245 |
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Author |
Luis Chuquimarca, Renzo Pacheco, Paula Gonzalez, Boris Vintimilla & Sergio Velastin |
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Title |
Fruit defect detection using CNN models with real and virtual data. |
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Conference Article |
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2023 |
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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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cidis @ cidis @ |
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203 |
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Author |
Luis Jacome-Galarza, Monica Villavicencio-Cabezas, Miguel Realpe-Robalino, Jose Benavides-Maldonado |
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Title |
Software Engineering and Distributed Computing in image processing intelligent systems: a systematic literature review. |
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Conference Article |
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2021 |
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19th LACCEI International Multi-Conference for Engineering, Education, and Technology |
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processing, software engineering, deep learning, intelligent vision systems, cloud computing. |
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Deep learning is experiencing an upward technology trend that is revolutionizing intelligent systems in several domains, such as image and speech recognition, machine translation, social network filtering, and the like. By reviewing a total of 80 studies reported from 2016 to 2020, the present article evaluates the application of software engineering to the field
of intelligent image processing systems, it also offers insights about aspects related to distributed computing for this type of systems. Results indicate that several topics of software engineering are mostly applied when academics are involved in developing projects associated to this kind of intelligent systems. The findings provide evidences that Apache Spark is the most
utilized distributed computing framework for image processing. In addition, Tensorflow is a popular framework used to build convolutional neural networks, which are the prevailing deep learning algorithms used in intelligent image processing systems.
Also, among big cloud providers, Amazon Web Services is the preferred computing platform across the industry sectors, followed by Google cloud. |
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English |
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cidis @ cidis @ |
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154 |
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Author |
Lukas Danev; Marten Hamann; Nicolas Fricke; Tobias Hollarek; Dennys Paillacho |
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Title |
Development of animated facial expression to express emotions in a robot: RobotIcon. |
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2017 |
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IEEE Ecuador Technical Chapter Meeting (ETCM) |
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2017-January |
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1-6 |
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gtsi @ user @ |
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71 |
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