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Author | Luis Chuquimarca, Renzo Pacheco, Paula Gonzalez, Boris Vintimilla & Sergio Velastin | ||||
Title | Fruit defect detection using CNN models with real and virtual data. | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2023 | Abbreviated Journal | |
Volume | Issue | Pages | 272 - 279 | ||
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Call Number | cidis @ cidis @ | Serial | 203 | ||
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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 | ||
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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. | ||||
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Language | English | Summary Language | English | Original Title | |
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Call Number | cidis @ cidis @ | Serial | 48 | ||
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Author | Xavier Soria; Angel D. Sappa | ||||
Title | Improving Edge Detection in RGB Images by Adding NIR Channel. | Type | Conference Article | ||
Year | 2018 | Publication | 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) | Abbreviated Journal | |
Volume | Issue | Pages | 266-273 | ||
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Call Number | gtsi @ user @ | Serial | 95 | ||
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Author | Emmanuel Moran Barreiro & Boris Vintimilla | ||||
Title | Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves | Type | Conference Article | ||
Year | 2023 | Publication | Lecture Notes in Computer Science. 26th Iberoamerican Congress on Pattern Recognition | Abbreviated Journal | |
Volume | 14469 LNCS | Issue | Pages | 257 - 271 | |
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Call Number | cidis @ cidis @ | Serial | 222 | ||
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Author | Miguel Realpe; Boris X. Vintimilla; L. Vlacic | ||||
Title | Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion. | Type | Conference Article | ||
Year | 2015 | Publication | IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, 2015. | Abbreviated Journal | |
Volume | Issue | Pages | 253-258 | ||
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Abstract | Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system. | ||||
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Call Number | cidis @ cidis @ | Serial | 37 | ||
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Author | G.A. Rubio; Wilton Agila | ||||
Title | Transients analysis in Proton Exchange Membrane Fuel Cells: A critical review | Type | Conference Article | ||
Year | 2019 | Publication | 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania | Abbreviated Journal | |
Volume | Issue | Pages | 249-252 | ||
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Abstract | When a proton exchange fuel cell operates it produces in addition to electrical energy, heat and water as sub products, which impact on the performance of the cell. This paper analyzes the issue of transients and proposes a model that describes the dynamic operation of the fuel cell. The model considers the transients produced by electrochemical reactions, by flow water and by heat transfer. Two-phase flow transients result in increased the parasitic power losses and thermal transients may result in flooding or dryout of the GDL and membrane, understanding transient behavior is critical for reliable and predictable performance from the cell. |
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Call Number | gtsi @ user @ | Serial | 111 | ||
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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 | ||
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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. | ||||
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Call Number | gtsi @ user @ | Serial | 97 | ||
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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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Call Number | gtsi @ user @ | Serial | 107 | ||
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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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Call Number | gtsi @ user @ | Serial | 68 | ||
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Author | G.A. Rubio; Wilton Agila | ||||
Title | Sustainable Energy: A Strategic View of Fuel Cells | Type | Conference Article | ||
Year | 2019 | Publication | 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania | Abbreviated Journal | |
Volume | Issue | Pages | 239-243 | ||
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Abstract | Based on the model of the proton exchange fuel cell in a strategic context, this document develops the issue of energy as one of the pillars to achieve the sustainability of our planet, considering the future scenarios up to the year 2060 of the situation energy, hydrogen as a strategic vector and the contribution of the fuel cell in solving the serious problems of environmental pollution and economic inequity that humanity faces; for its application in the energy generation, telecommunications and vehicle manufacturing industries. |
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Call Number | gtsi @ user @ | Serial | 110 | ||
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