|
Records |
Links |
|
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 |
|
|
|
|
Author |
Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin |


|
|
Title  |
Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models |
Type |
Conference Article |
|
Year |
2024 |
Publication |
14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
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 |
979-835037565-7 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
245 |
|
Permanent link to this record |
|
|
|
|
Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |

|
|
Title  |
Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture |
Type |
Conference Article |
|
Year |
2017 |
Publication |
19th International Conference on Image Analysis and Processing. |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
287-297 |
|
|
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 |
gtsi @ user @ |
Serial |
66 |
|
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 |
Velesaca, H.O., Suárez, P. L., Mira, R., & Sappa, A.D. |

|
|
Title  |
Computer Vision based Food Grain Classification: a Comprehensive Survey |
Type |
Journal Article |
|
Year |
2021 |
Publication |
In Computers and Electronics in Agriculture Journal. (Article number 106287) |
Abbreviated Journal |
|
|
|
Volume |
Vol. 187 |
Issue |
|
Pages |
|
|
|
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 |
159 |
|
Permanent link to this record |
|
|
|
|
Author |
Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco |

|
|
Title  |
Computer vision for image understanding. A comprehensive review |
Type |
Conference Article |
|
Year |
2019 |
Publication |
International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
248-259 |
|
|
Keywords |
|
|
|
Abstract |
Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction. |
|
|
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 |
97 |
|
Permanent link to this record |
|
|
|
|
Author |
Michael Teutsch, Angel Sappa & Riad Hammoud |

|
|
Title  |
Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches |
Type |
Journal Article |
|
Year |
2021 |
Publication |
Synthesis Lectures on Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
Vol. 10 No. 2 |
Issue |
|
Pages |
pp. 138 |
|
|
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 |
166 |
|
Permanent link to this record |
|
|
|
|
Author |
M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé |

|
|
Title  |
Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains |
Type |
Conference Article |
|
Year |
2015 |
Publication |
Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
2488 - 2495 |
|
|
Keywords |
Birds, Training, Legged locomotion, Visualization, Histograms, Object recognition, Gaussian mixture model |
|
|
Abstract |
In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE |
Place of Publication |
Hamburg, Germany |
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 |
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
41 |
|
Permanent link to this record |
|
|
|
|
Author |
Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa |


|
|
Title  |
Corn Kernel Classification From Few Training Samples |
Type |
Journal Article |
|
Year |
2023 |
Publication |
In journal Artificial Intelligence in Agriculture |
Abbreviated Journal |
|
|
|
Volume |
Vol. 9 |
Issue |
|
Pages |
pp. 89-99 |
|
|
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 |
25897217 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
cidis @ cidis @ |
Serial |
223 |
|
Permanent link to this record |
|
|
|
|
Author |
Jacome-Galarza L.-R |

|
|
Title  |
Crop yield prediction utilizing multimodal deep learning |
Type |
Conference Article |
|
Year |
2021 |
Publication |
16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada. |
|
|
Abstract |
La agricultura de precisión es una práctica vital para
mejorar la producción de cosechas. El presente trabajo tiene
como objetivo desarrollar un modelo multimodal de aprendizaje
profundo que es capaz de producir un mapa de salud de
cosechas. El modelo recibe como entradas imágenes multiespectrales
y datos de sensores de campo (humedad,
temperatura, estado del suelo, etc.) y crea un mapa de
rendimiento de la cosecha. La utilización de datos multimodales
tiene como finalidad extraer patrones ocultos del estado de salud
de las cosechas y de esta manera obtener mejores resultados que
los obtenidos mediante los índices de vegetación. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
Español |
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 |
150 |
|
Permanent link to this record |