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Spencer Low, O. N., Angel D. Sappa, Erik Blasch, Nathan Inkawhich. (2023). Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results – PBVS 2023. In 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 (Vol. 2023-June, pp. 515–523).
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Rafael E. Rivadeneira, A. D. S., Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye. (2023). Thermal Image Super-Resolution Challenge Results – PBVS 2023. In 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 (Vol. 2023-June, pp. 470–478).
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Spencer Low, O. N., Angel D. Sappa, Erik Blasch, Nathan Inkawhich. (2023). Multi-modal Aerial View Object Classification Challenge Results – PBVS 2023. In 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 (Vol. 2023-June, pp. 412–421).
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Mehri, A., Ardakani, P.B., Sappa, A.D. (2021). LiNet: A Lightweight Network for Image Super Resolution. In 25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 (pp. 7196–7202).
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Patricia Suarez, A. S. (2024). Depth-Conditioned Thermal-like Image Generation. In Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS).
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Angel D. Sappa, S. L., Oliver Nina, Erik Blasch, Dylan Bowald & Nathan Inkawhich. (2024). Multi-modal Aerial View Image Challenge: SAR Classification. In Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition.
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Angel D. Sappa, S. L., Oliver Nina, Erik Blasch, Dylan Bowald & Nathan Inkawhich. (2024). Multi-modal Aerial View Image Challenge: Sensor Domain Translation. In Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition.
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Rafael E. Rivadeneira, A. D. S., Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Peilin Chen & Shiqi Wang. (2024). Thermal Image Super Resolution Challenge Results – PBVS 2024. In Accepted in 20th IEEE Workshop on Perception Beyond the Visible Spectrum of the 2024 Conference on Computer Vision and Pattern Recognition.
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Rafael E. Rivadeneira, A. D. S., Boris X. Vintimilla, Jin Kim, Dogun Kim et al. (2022). Thermal Image Super-Resolution Challenge Results- PBVS 2022. In Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 349–357).
Abstract: This paper presents results from the third Thermal Image
Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop.
The challenge uses the same thermal image dataset as the
first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was
kept aside for testing. The evaluation tasks were to measure
the PSNR and SSIM between the SR image and the ground
truth (HR thermal noisy image downsampled by four), and
also to measure the PSNR and SSIM between the SR image
and the semi-registered HR image (acquired with another
camera). The results outperformed those from last year’s
challenge, improving both evaluation metrics. This year,
almost 100 teams participants registered for the challenge,
showing the community’s interest in this hot topic.
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Armin Mehri, & Angel D. Sappa. (2019). Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples. In Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States (pp. 971–979).
Abstract: This paper presents a novel approach for colorizing
near infrared (NIR) images. The approach is based on
image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored
networks that require less computation times, converge
faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation
metrics—and qualitatively evaluated showing considerable
improvements with respect to the state of the art
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