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Author | Angel D. Sappa. | ||||
Title | ICT Applications for Smart Cities | Type | Book Chapter | ||
Year | 2022 | Publication | Intelligent Systems Reference Library | Abbreviated Journal | BOOK |
Volume | 224 | Issue | Pages | ||
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Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 198 | ||
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Author | Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Jin Kim, Dogun Kim et al. | ||||
Title | Thermal Image Super-Resolution Challenge Results- PBVS 2022. | Type | Conference Article | ||
Year | 2022 | Publication | Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. | Abbreviated Journal | CONFERENCE |
Volume | 2022-June | Issue | Pages | 349-357 | |
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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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Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 175 | ||
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Author | Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez | ||||
Title | SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. | Type | Journal Article | ||
Year | 2020 | Publication | Abbreviated Journal | In Sensors | |
Volume | Vol. 2020-August | Issue | 16 | Pages | pp. 1-23 |
Keywords | object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities | ||||
Abstract | This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images oers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the diculty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with dierent types of semantic segmentation networks and using the same evaluation metrics is also included. |
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Language | English | Summary Language | English | Original Title | |
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ISSN | ISBN | 14248220 | Medium | ||
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Notes | Approved | no | |||
Call Number | cidis @ cidis @ | Serial | 133 | ||
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