| Records |
| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
| Title |
Learning to Colorize Infrared Images |
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Conference Article |
| Year |
2017 |
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15th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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cidis @ cidis @ |
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58 |
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| Author |
Cristhian A. Aguilera; Xaver Soria; Angel D. Sappa; Ricardo Toledo |
| Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
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Conference Article |
| Year |
2017 |
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15th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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619 |
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155-163 |
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cidis @ cidis @ |
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59 |
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| 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 |
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2017 |
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2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
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1-6 |
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cidis @ cidis @ |
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60 |
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| Author |
Dennis G. Romero; Roberto Yoncon; Angel Guale; Bonny Bayot; Fanny Panchana |
| Title |
Evaluación de técnicas de clasificación orientadas a la identificación automática de órganos del camarón a partir de imágenes histológicas |
Type |
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| Year |
2017 |
Publication |
15th LACCEI International Multi-Conference for Engineering, Education, and Technology |
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2017-July |
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1-6 |
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cidis @ cidis @ |
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61 |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
| Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture. |
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Conference Article |
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2017 |
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13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) |
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2017-July |
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212-217 |
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no |
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cidis @ cidis @ |
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62 |
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| Author |
Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa |
| Title |
Deep learning based camera pose estimation in multi-view environment. |
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| Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) |
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224-228 |
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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. |
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gtsi @ user @ |
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93 |
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| Author |
Alex Ferrin; Julio Larrea; Miguel Realpe; Daniel Ochoa |
| Title |
Detection of utility poles from noisy Point Cloud Data in Urban environments. |
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Conference Article |
| Year |
2018 |
Publication |
Artificial Intelligence and Cloud Computing Conference (AICCC 2018) |
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53-57 |
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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. |
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94 |
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| 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 |
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971-979 |
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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 |
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gtsi @ user @ |
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105 |
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
| Title |
Image Vegetation Index through a Cycle Generative Adversarial Network |
Type |
Conference Article |
| Year |
2019 |
Publication |
Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States |
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1014-1021 |
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This paper proposes a novel approach to estimate the
Normalized Difference Vegetation Index (NDVI) just from
an RGB image. The NDVI values are obtained by using
images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The
cycled GAN network is able to obtain a NIR image from
a given gray scale image. It is trained by using unpaired
set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are
obtained from the provided RGB images). Then, the NIR
image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous
approaches are also provided. |
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gtsi @ user @ |
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106 |
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| Author |
Angel Morera; Angel Sánchez; Angel D. Sappa; José F. Vélez |
| Title |
Robust Detection of Outdoor Urban Advertising Panels in Static Images. |
Type |
Conference Article |
| Year |
2019 |
Publication |
17th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2019); Ávila, España. Communications in Computer and Information Science |
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| Volume |
1047 |
Issue |
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246-256 |
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One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising
panels. For such a purpose, a previous stage is to accurately detect and
locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based
on a deep neural network architecture that minimizes the number of
false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection
over Union (IoU) accuracy metric make this proposal applicable in real
complex urban images. |
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107 |
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