| |
Records |
Links |
|
Author |
Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo |
|
| |
Title |
Learning crossspectral similarity measures with deep convolutional neural networks |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops |
Abbreviated Journal |
|
|
| |
Volume  |
|
Issue |
|
Pages |
267-275 |
|
| |
Keywords |
|
|
| |
Abstract |
The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains. |
|
| |
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 |
48 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Monica Villavicencio; Alain Abran |
|
| |
Title |
Educational Issues in the Teaching of Software Measurement in Software Engineering Undergraduate Programs |
Type |
Conference Article |
| |
Year |
2011 |
Publication |
Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
239-244 |
|
| |
Keywords |
measurement; software engineering; higher education |
|
| |
Abstract |
In mature engineering disciplines and science, mathematics and measurement are considered as important subjects to be taught in university programs. This paper discusses about these subjects in terms of their respective meanings and complementarities. It also presents a discussion regarding their maturity, relevance and innovations in their teaching in engineering programs. This paper pays special attention to the teaching of software measurement in higher education, in particular with respect to mathematics and measurement in engineering in general. The findings from this analysis will be useful for researchers and educators interested in the enhancement of educational issues related to software measurement. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IEEE |
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 |
gtsi @ user @ |
Serial |
68 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Angely Oyola; Dennis G. Romero; Boris X. Vintimilla |
|
| |
Title |
A Dijkstra-based algorithm for selecting the Shortest-Safe Evacuation Routes in dynamic environments (SSER) |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
131-135 |
|
| |
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 |
55 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
|
| |
Title |
Cross-spectral Image Patch Similarity using Convolutional Neural Network |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1-5 |
|
| |
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 |
57 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Angel J. Valencia; Roger M. Idrovo; Angel D. Sappa; Douglas Plaza G.; Daniel Ochoa |
|
| |
Title |
A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers |
Type |
Conference Article |
| |
Year |
2017 |
Publication |
2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
1-6 |
|
| |
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 |
60 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
Marjorie Chalen; Boris X. Vintimilla |
|
| |
Title |
Towards Action Prediction Applying Deep Learning |
Type |
Journal Article |
| |
Year |
2019 |
Publication |
Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
pp. 1-3 |
|
| |
Keywords |
action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm. |
|
| |
Abstract |
Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task. |
|
| |
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 |
129 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Armin Mehri; Angel D. Sappa |
|
| |
Title |
Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
971-979 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper presents a novel approach for colorizing
near infrared (NIR) images. The approach is based on
image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored
networks that require less computation times, converge
faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation
metrics—and qualitatively evaluated showing considerable
improvements with respect to the state of the art |
|
| |
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 |
105 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. |
|
| |
Title |
Thermal Image SuperResolution through Deep Convolutional Neural Network. |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
417-426 |
|
| |
Keywords |
|
|
| |
Abstract |
Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset |
|
| |
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 |
103 |
|
| Permanent link to this record |
