| Records |
| Author |
Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye |
| Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2023 |
Type |
Conference Article |
| Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition (CVPR 2023) Vancouver, 18-28 junio 2023 |
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| Volume |
2023-June |
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Pages |
470 - 478 |
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| ISSN |
21607508 |
ISBN |
979-835030249-3 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
210 |
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| Author |
Omar Coello, Moisés Coronel, Darío Carpio, Boris X. Vintimilla & Luis Chuquimarca |
| Title |
Enhancing Apple’s Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging |
Type |
Conference Article |
| Year |
2024 |
Publication |
14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024 |
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ISBN |
979-835037565-7 |
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no |
| Call Number |
cidis @ cidis @ |
Serial |
244 |
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| Author |
Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin |
| Title |
Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models |
Type |
Conference Article |
| Year |
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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no |
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cidis @ cidis @ |
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245 |
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| Author |
Emmanuel F. Morán, Boris X. Vintimilla, Miguel A. Realpe |
| Title |
Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves |
Type |
Conference Article |
| Year |
2024 |
Publication |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 26th Iberoamerican Congress on Pattern Recognition, CIARP 2023 Coimbra 27 – 30 November 2023 |
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| Volume |
Vol. 14470 |
Issue |
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Pages |
257–271 |
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03029743 |
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978-303149017-0 |
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no |
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cidis @ cidis @ |
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249 |
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| Author |
Kevin E. Munoz, Loberlly N. Salazar, Steven S. Araujo, and Boris X. Vintimilla |
| Title |
Stereo Vision Techniques: A Comparative Study of Traditional and Machine Learning-Based Approaches |
Type |
Conference Article |
| Year |
2025 |
Publication |
5th International Conference on Computer Vision and Robotics CVR 2025 |
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no |
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cidis @ cidis @ |
Serial |
280 |
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| Author |
Kevin E. Muñoz Loberlly N. Salazar Steven S. Araujo Boris X. Vintimilla |
| Title |
Detecting and Characterizing Human Interactions to EnhanceHuman-Robot Engagement |
Type |
Conference Article |
| Year |
2025 |
Publication |
3rd International Conference on Robotics, Control and Vision Engineering RCVE 2025 |
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no |
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cidis @ cidis @ |
Serial |
281 |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
| Title |
Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images |
Type |
Conference Article |
| Year |
2018 |
Publication |
International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing |
Abbreviated Journal |
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| Volume |
721 |
Issue |
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Pages |
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| Abstract |
This paper proposes a novel approach to use cross-spectral
images to achieve a better performance with the proposed Adaptive Harris
corner detector comparing its obtained results with those achieved
with images of the visible spectra. The images of urban, field, old-building
and country category were used for the experiments, given the variety of
the textures present in these images, with which the complexity of the
proposal is much more challenging for its verification. It is a new scope,
which means improving the detection of characteristic points using crossspectral
images (NIR, G, B) and applying pruning techniques, the combination
of channels for this fusion is the one that generates the largest
variance based on the intensity of the merged pixels, therefore, it is that
which maximizes the entropy in the resulting Cross-spectral images.
Harris is one of the most widely used corner detection algorithm, so
any improvement in its efficiency is an important contribution in the
field of computer vision. The experiments conclude that the inclusion of
a (NIR) channel in the image as a result of the combination of the spectra,
greatly improves the corner detection due to better entropy of the
resulting image after the fusion, Therefore the fusion process applied to
the images improves the results obtained in subsequent processes such as
identification of objects or patterns, classification and/or segmentation. |
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1 |
Approved |
no |
| Call Number |
gtsi @ user @ |
Serial |
84 |
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