| |
Records |
Links |
|
Author |
Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa |
|
| |
Title |
Deep learning based camera pose estimation in multi-view environment. |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
224-228 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. |
|
| |
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 |
gtsi @ user @ |
Serial |
93 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa |
|
| |
Title |
Detection of utility poles from noisy Point Cloud Data in Urban environments. |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
Artificial Intelligence and Cloud Computing Conference (AICCC 2018) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
53-57 |
|
| |
Keywords |
|
|
| |
Abstract |
In recent years 3D urban maps have become more common, thus providing complex point clouds that include diverse urban furniture such as pole-like objects. Utility poles detection in urban environment is of particular interest for electric utility companies in order to maintain an updated inventory for better planning and management. The present study develops an automatic method for the detection of utility poles from noisy point cloud data of Guayaquil – Ecuador, where many poles are located next to buildings, or houses are built until the border of the sidewalk getting very close to poles, which increases the difficulty of discriminating poles, walls, columns, fences and building corners. |
|
| |
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 |
gtsi @ user @ |
Serial |
94 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
|
| |
Title |
Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing |
Abbreviated Journal |
|
|
| |
Volume |
721 |
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
This paper proposes a novel approach to use cross-spectral
images to achieve a better performance with the proposed Adaptive Harris
corner detector comparing its obtained results with those achieved
with images of the visible spectra. The images of urban, field, old-building
and country category were used for the experiments, given the variety of
the textures present in these images, with which the complexity of the
proposal is much more challenging for its verification. It is a new scope,
which means improving the detection of characteristic points using crossspectral
images (NIR, G, B) and applying pruning techniques, the combination
of channels for this fusion is the one that generates the largest
variance based on the intensity of the merged pixels, therefore, it is that
which maximizes the entropy in the resulting Cross-spectral images.
Harris is one of the most widely used corner detection algorithm, so
any improvement in its efficiency is an important contribution in the
field of computer vision. The experiments conclude that the inclusion of
a (NIR) channel in the image as a result of the combination of the spectra,
greatly improves the corner detection due to better entropy of the
resulting image after the fusion, Therefore the fusion process applied to
the images improves the results obtained in subsequent processes such as
identification of objects or patterns, classification and/or segmentation. |
|
| |
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 |
1 |
Approved |
no |
|
| |
Call Number |
gtsi @ user @ |
Serial |
84 |
|
| Permanent link to this record |
