| Records |
| Author |
Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
| Title |
Multi-modal Aerial View Object Classification Challenge Results – PBVS 2023 |
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Conference Article |
| Year |
2023 |
Publication |
19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 |
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2023-June |
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412 - 421 |
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| ISSN |
21607508 |
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979-835030249-3 |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
Serial |
212 |
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| Author |
Gisel Bastidas-Guacho, Patricio Moreno-Vallejo, Boris Vintimilla, Angel D. Sappa |
| Title |
Application on the Loop of Multimodal Image Fusion: Trends on Deep-Learning Based Approaches |
Type |
Conference Article |
| Year |
2023 |
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IEEE 13th International Conference on Pattern Recognition Systems ICPRS 2023, julio 4-7 |
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979-835033337-4 |
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no |
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cidis @ cidis @ |
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213 |
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| Author |
Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa |
| Title |
Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach |
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Conference Article |
| Year |
2023 |
Publication |
17th International Conference On Signal Image Technology & Internet Based System |
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no |
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cidis @ cidis @ |
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224 |
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| Author |
Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa |
| Title |
Tiny and Efficient Model for the Edge Detection Generalization |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 |
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1356 - 1365 |
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no |
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cidis @ cidis @ |
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229 |
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| Author |
Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
| Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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Journal Article |
| Year |
2018 |
Publication |
In Sensors 2018 |
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| Volume |
Vol. 11 |
Issue |
Issue 11 |
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| Abstract |
In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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no |
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gtsi @ user @ |
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89 |
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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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no |
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gtsi @ user @ |
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63 |
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| Author |
Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo |
| Title |
Cross-Spectral Local Descriptors via Quadruplet Network |
Type |
Journal Article |
| Year |
2017 |
Publication |
In Sensors Journal |
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| Volume |
Vol. 17 |
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Pages |
pp. 873 |
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no |
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gtsi @ user @ |
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64 |
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| Author |
Victor Santos; Angel D. Sappa; Miguel Oliveira |
| Title |
Special Issue on Autonomous Driving an Driver Assistance Systems |
Type |
Journal Article |
| Year |
2017 |
Publication |
In Robotics and Autonomous Systems Journal |
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Vol. 91 |
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pp. 208-209 |
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no |
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gtsi @ user @ |
Serial |
65 |
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| Author |
Xavier Soria; Angel D. Sappa; Riad Hammoud |
| Title |
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059. |
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Journal Article |
| Year |
2018 |
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Vol. 18 |
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Issue 7 |
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| Abstract |
Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
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no |
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gtsi @ user @ |
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96 |
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| Author |
Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa |
| Title |
Feature Point Descriptors: Infrared and Visible Spectra |
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Journal Article |
| Year |
2014 |
Publication |
Sensors Journal |
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Vol. 14 |
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pp. 3690-3701 |
| Keywords |
cross-spectral imaging; feature point descriptors |
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This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. |
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English |
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English |
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Approved |
no |
| Call Number |
cidis @ cidis @ |
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28 |
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