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Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
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Title |
Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results – PBVS 2023 |
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Conference Article |
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2023 |
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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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515 - 523 |
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21607508 |
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979-835030249-3 |
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cidis @ cidis @ |
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211 |
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Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye |
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Title |
Thermal Image Super-Resolution Challenge Results – PBVS 2023 |
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Conference Article |
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Year |
2023 |
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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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470 - 478 |
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21607508 |
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979-835030249-3 |
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cidis @ cidis @ |
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210 |
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Author |
Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich |
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Title |
Multi-modal Aerial View Object Classification Challenge Results – PBVS 2023 |
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Conference Article |
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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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21607508 |
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979-835030249-3 |
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cidis @ cidis @ |
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212 |
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Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca |
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Title |
Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem |
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Conference Article |
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2020 |
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The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 |
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4 |
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498-505 |
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Keywords |
Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters. |
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Abstract |
This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model
a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The
transfer learning consist of first training the network using pairs of images from the virtual-world scenario
considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight
of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose
estimation accuracy using the proposed model, as well as further improvements when the transfer learning
strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on
the training due to the reduced number of pairs of real-images on most of the public data sets. |
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978-989758402-2 |
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gtsi @ user @ |
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120 |
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