| |
Records |
Links |
|
Author |
Morocho-Cayamcela, M.E. |
|
| |
Title |
Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Electronic Letters on Computer Vision and Image Analysis (ELCVIA) |
Abbreviated Journal |
|
|
| |
Volume |
Vol. 19 |
Issue |
Issue 2 |
Pages |
pp. 17-21 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN  |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
140 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Rosero Vasquez Shendry |
|
| |
Title |
Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020. |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
|
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
615-625 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
145 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Viñán-Ludeña, M.S., Roberto Jacome Galarza, Montoya, L.R., Leon, A.V., & Ramírez, C.C. |
|
| |
Title |
Smart university: an architecture proposal for information management using open data for research projects. |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Advances in Intelligent Systems and Computing |
Abbreviated Journal |
|
|
| |
Volume |
1137 AISC, 2020 |
Issue |
|
Pages |
172-178 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
188 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. |
|
| |
Title |
Camera pose estimation in multi-view environments:from virtual scenarios to the real world |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
In Image and Vision Computing Journal. (Article number 104182) |
Abbreviated Journal |
|
|
| |
Volume |
Vol. 110 |
Issue |
|
Pages |
|
|
| |
Keywords |
Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments |
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
English |
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
cidis @ cidis @ |
Serial |
147 |
|
| Permanent link to this record |
