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Author | Xavier Soria; Angel D. Sappa; Riad Hammoud | ||||
Title | Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059. | Type | Journal Article | ||
Year | 2018 | Publication | Abbreviated Journal | ||
Volume | Vol. 18 | Issue | Issue 7 | Pages | |
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Abstract | Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches. | ||||
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gtsi @ user @ | Serial | 96 | ||
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Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
Title | Cross-spectral image dehaze through a dense stacked conditional GAN based approach. | Type | Conference Article | ||
Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
Volume | Issue | Pages | 358-364 | ||
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Abstract | This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. | ||||
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gtsi @ user @ | Serial | 92 | ||
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Author | Dennys Paillacho; Cecilio Angulo; Marta Díaz. | ||||
Title | An Exploratory Study of Group-Robot Social Interactions in a Cultural Center | Type | Conference Article | ||
Year | 2015 | Publication | IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, International Conference on, Hamburg, Germany, 2015 | Abbreviated Journal | |
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Abstract | This article describes an exploratory study of social human-robot interaction with the experimental robotic platform MASHI. The experiences were carried out in La B`obila Cultural Center in Barcelona, Spain to study the visitor preferences, characterize the groups and their spatial relationships in this open and unstructured environment. Results showed that visitors prefers to play and dialogue with the robot. Children have the highest interest in interacting with the robot, more than young and adult visitors. Most of the groups consisted of more than 3 visitors, however the size of the groups during interactions was continuously changed. In static situations, the observed spatial relationships denotes a social cohesion in the human-robot interactions. | ||||
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Language | English | Summary Language | Original Title | ||
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gtsi @ user @ | Serial | 67 | ||
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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. | ||||
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Publisher | IEEE | Place of Publication | Editor | ||
Language | English | Summary Language | English | Original Title | |
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gtsi @ user @ | Serial | 68 | ||
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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. | 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 | ||
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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. | ||||
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gtsi @ user @ | Serial | 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. | Type | Conference Article | ||
Year | 2018 | Publication | Artificial Intelligence and Cloud Computing Conference (AICCC 2018) | Abbreviated Journal | |
Volume | Issue | Pages | 53-57 | ||
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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. | ||||
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gtsi @ user @ | Serial | 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 | Abbreviated Journal | |
Volume | Issue | Pages | 971-979 | ||
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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 |
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gtsi @ user @ | Serial | 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 | Abbreviated Journal | |
Volume | Issue | Pages | 1014-1021 | ||
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Abstract | 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 @ | Serial | 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 | Abbreviated Journal | |
Volume | 1047 | Issue | Pages | 246-256 | |
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Abstract | 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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gtsi @ user @ | Serial | 107 | ||
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Author | José Reyes; Axel Godoy; Miguel Realpe. | ||||
Title | Uso de software de código abierto para fusión de imágenes agrícolas multiespectrales adquiridas con drones. | Type | Conference Article | ||
Year | 2019 | Publication | International Multi-Conference of Engineering, Education and Technology (LACCEI 2019); Montego Bay, Jamaica | Abbreviated Journal | |
Volume | 2019-July | Issue | Pages | ||
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Abstract | Los drones o aeronaves no tripuladas son muy útiles para la adquisición de imágenes, de forma mucho más simple que los satélites o aviones. Sin embargo, las imágenes adquiridas por drones deben ser combinadas de alguna forma para convertirse en información de valor sobre un terreno o cultivo. Existen diferentes programas que reciben imágenes y las combinan en una sola imagen, cada uno con diferentes características (rendimiento, precisión, resultados, precio, etc.). En este estudio se revisaron diferentes programas de código abierto para fusión de imágenes, con el ?n de establecer cuál de ellos es más útil, especí?camente para ser utilizado por pequeños y medianos agricultores en Ecuador. Los resultados pueden ser de interés para diseñadores de software, ya que al utilizar código abierto, es posible modi?car e integrar los programas en un ?ujo de trabajo más simpli?cado. Además, que permite disminuir costos debido a que no requiere de pagos de licencias para su uso, lo cual puede repercutir en un mayor acceso a la tecnología para los pequeños y medianos agricultores. Como parte de los resultados de este estudio se ha creado un repositorio de acceso público con algoritmos de pre-procesamiento necesarios para manipular las imágenes adquiridas por una cámara multiespectral y para luego obtener un mapa completo en formatos RGB, CIR y NDVI. | ||||
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gtsi @ user @ | Serial | 102 | ||
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