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Henry Velesaca, Boris Vintimilla, Jorge Vulgarin, Coen Antens & Alberto Rubio Pérez |
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Title |
Deep Learning-based Multimodal Sensing Framework for AntiSpoofing Systems |
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2024 |
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In Fourth International Conference on Innovations in Computational Intelligence and Computer Vision (ICICV 2024) |
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cidis @ cidis @ |
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238 |
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Tyrone Rodríguez, Adriana Guilindro, Paolo Piedrahita & Miguel Realpe |
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Title |
Towards Birds Conservation in Dry Forest Ecosystems through Audio Recognition via Deep Learning |
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2024 |
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In 9th International Congress on Information and Communication Technology ICICT 2024 |
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cidis @ cidis @ |
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239 |
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Patricia Suarez, Angel D. Sappa |
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Title |
A Generative Model for Guided Thermal Image Super-Resolution |
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2024 |
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In 19th International Conference on Computer Vision Theory and Applications VISAPP 2024 |
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cidis @ cidis @ |
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240 |
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Henry Velesaca Lara, Patricia Suarez, Darío Carpio & Angel Sappa |
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Title |
Fruit Grading based on Deep Learning and Active Vision System |
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Conference Article |
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2024 |
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Accepted in CIIA – II International Conference of Applied Industrial Engineering |
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cidis @ cidis @ |
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241 |
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Henry Velesaca Lara, Juan Antonio Holgado & José Miguel Gutiérrez |
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Title |
Optimizing Smart Factory Operations: A Methodological Approach to Industrial System Implementation based on OPC-UA |
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Conference Article |
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2024 |
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Accepted in CIIA – II International Conference of Applied Industrial Engineering |
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cidis @ cidis @ |
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242 |
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Author |
Patricia Suarez, Angel Sappa |
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Title |
Depth-Conditioned Thermal-like Image Generation |
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Conference Article |
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2024 |
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Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS) |
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cidis @ cidis @ |
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243 |
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Author |
Omar Coello, Moisés Coronel, Darío Carpio, Boris X. Vintimilla & Luis Chuquimarca |
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Title |
Enhancing Apple’s Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging |
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Conference Article |
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2024 |
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Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS) |
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cidis @ cidis @ |
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244 |
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Author |
Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin |
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Title |
Classifying Healthy and Defective Fruits with a Siamese Architecture and CNN Models |
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Conference Article |
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2024 |
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Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS) |
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cidis @ cidis @ |
Serial |
245 |
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Author |
Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco |
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Title |
Computer vision for image understanding. A comprehensive review |
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2019 |
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International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador |
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248-259 |
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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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gtsi @ user @ |
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97 |
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Author |
Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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Title |
Melamine faced panels defect classification beyond the visible spectrum. |
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2018 |
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In Sensors 2018 |
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Vol. 11 |
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Issue 11 |
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Abstract |
In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
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gtsi @ user @ |
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89 |
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