|
|
Armin Mehri, Parichehr Behjati, & Angel Domingo Sappa. (2023). TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution. IEEE Access, Vol. 11.
|
|
|
|
Xavier Soria, A. S., Patricio Humanante, Arash Akbarinia. (2023). Dense extreme inception network for edge detection. Pattern Recognition, Vol. 139.
|
|
|
|
Abel Rubio, W. A., Leandro González & Jonathan Aviles-Cedeno. (2023). Distributed Intelligence in Autonomous PEM Fuel Cell Control. Energies 2023, Vol. 16(Issue 12).
|
|
|
|
Patricia Súarez, H. V., Dario Carpio & Angel Sappa. (2023). Corn Kernel Classification From Few Training Samples. In journal Artificial Intelligence in Agriculture, Vol. 9, pp. 89–99.
|
|
|
|
Armin Mehri, P. B., Dario Carpio, and Angel D. Sappa. (2023). SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution. IEEE access, Vol. 11, 121457–121469.
|
|
|
|
Rafael E. Rivadeneira, A. D. S., Vintimilla B. X. and Hammoud R. (2022). A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super- Resolution. Sensors, Vol. 22(Issue 6).
|
|
|
|
Daniela Rato, M. O., Victor Santos, Manuel Gomes & Angel Sappa. (2022). A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. Journal of Manufacturing Systems, Vol. 64, pp. 497–507.
|
|
|
|
Xavier Soria, G. P. - J. & A. S. (2022). LDC: Lightweight Dense CNN for Edge Detection. IEEE Access journal, Vol. 10, pp. 68281–68290.
|
|
|
|
Ulises Gildardo Quiroz Antúnez, A. I. M. R., María Fernanda Calderón Vega, Adán Guillermo Ramírez García. (2022). APTITUDE OF COFFEE (COFFEA ARABICA L.) AND CACAO (THEOBROMA CACAO L.) CROPS CONSIDERING CLIMATE CHANGE. Granja, Vol. 36(Issue 2).
|
|
|
|
Charco, J. L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. (2021). Camera pose estimation in multi-view environments:from virtual scenarios to the real world. In Image and Vision Computing Journal. (Article number 104182), Vol. 110.
Abstract: This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired
images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of
overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly
trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the
relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity
on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the
scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used,
highlighting the importance on the similarity between virtual-real scenarios.
|
|
