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Rafael E. Rivadeneira, Angel D. Sappa and Riad Hammoud, Jiyong Rao, Hang Zhong, Yu Wang, Shengjie Zhao & et. |
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Title |
Thermal Image Super- Resolution Challenge Results – PBVS 2025 |
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Conference Article |
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2025 |
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 |
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cidis @ cidis @ |
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274 |
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Author |
Wassim El Ahmar, Angel D. Sappa and Riad Hammoud |
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Title |
Thermal Pedestrian MultipleObject Tracking Challenge (TPMOT) |
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Conference Article |
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2025 |
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 |
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cidis @ cidis @ |
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275 |
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Nathan Inkawhich, Claire Thorp, Justice Wheelwright, Oliver Nina, Dylan Bowald, Angel Sappa, Erik Blasch |
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Title |
4th Multi-modal Aerial View Image Challenge: SAR CLASSIFICATION – PBVS 2025 |
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Journal Article |
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2025 |
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 |
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cidis @ cidis @ |
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276 |
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Dylan Bowald, Justice Wheelwright, Oliver Nina, Angel Sappa, Riad Hammoud, Erik Blasch, Nathan Inkawhich |
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Title |
3th Multi-modal Aerial View Image Challenge: Sensor Domain Translation – PBVS 2025 |
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2025 |
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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops CVPRW 2025 |
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Call Number |
cidis @ cidis @ |
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277 |
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Author |
Patricia Suarez & Angel D. Sappa |
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Title |
Lightweight Architecture for Fruit Quality Estimation in the Infrared Domain |
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Year |
2025 |
Publication |
5th International Conference on Innovations in Computational Intelligence and Computer Vision ICICV 2025 |
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Call Number |
cidis @ cidis @ |
Serial |
278 |
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Author |
Henry O. Velesaca Hector Villegas, and Angel D. Sappa |
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Title |
Exploring Camouflaged Object Detection Techniques for Invasive Vegetation Monitoring |
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Conference Article |
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Year |
2025 |
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14th International Conference on Data Science, Technology and Applications DATA 2025 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
279 |
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Author |
Kevin E. Munoz, Loberlly N. Salazar, Steven S. Araujo, and Boris X. Vintimilla |
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Title |
Stereo Vision Techniques: A Comparative Study of Traditional and Machine Learning-Based Approaches |
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Conference Article |
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Year |
2025 |
Publication |
5th International Conference on Computer Vision and Robotics CVR 2025 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
280 |
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Kevin E. Muñoz Loberlly N. Salazar Steven S. Araujo Boris X. Vintimilla |
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Title |
Detecting and Characterizing Human Interactions to EnhanceHuman-Robot Engagement |
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Conference Article |
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Year |
2025 |
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3rd International Conference on Robotics, Control and Vision Engineering RCVE 2025 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
281 |
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Author |
Mildred Cruz; Cristhian A. Aguilera; Boris X. Vintimilla; Ricardo Toledo; Ángel D. Sappa |
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Title |
Cross-spectral image registration and fusion: an evaluation study |
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Conference Article |
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Year |
2015 |
Publication |
2nd International Conference on Machine Vision and Machine Learning |
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331 |
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Keywords |
multispectral imaging; image registration; data fusion; infrared and visible spectra |
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Abstract |
This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Computer Vision Center |
Place of Publication |
Barcelona, Spain |
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English |
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English |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
35 |
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Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
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Title |
Sensor Fault Detection and Diagnosis for autonomous vehicles |
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Conference Article |
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Year |
2015 |
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2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 |
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30 |
Issue |
MATEC Web of Conferences |
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1-6 |
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Abstract |
In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor. |
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EDP Sciences |
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English |
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English |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
42 |
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