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Emmanuel F. Morán, Boris X. Vintimilla, Miguel A. Realpe |
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Towards a Robust Solution for the Supermarket Shelf Audit Problem: Obsolete Price Tags in Shelves |
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2024 |
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 26th Iberoamerican Congress on Pattern Recognition, CIARP 2023 Coimbra 27 – 30 November 2023 |
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Vol. 14470 |
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257–271 |
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03029743 |
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978-303149017-0 |
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cidis @ cidis @ |
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249 |
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Leo Ramos & Angel D. Sappa |
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Title |
Multispectral Semantic Segmentation for Land Cover Classification: An Overview |
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2024 |
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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Vol. 17 |
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14295-14336 |
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cidis @ cidis @ |
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250 |
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Tommy David Beltran Borbor, Raul Josue Villao Rodriguez, Luis Enrique Chuquimarca Jiménez, Boris Xavier Vintimilla Burgos & Sergio Alejandro Velastin |
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Title |
Fruit Deformity Classification through Single-Input and Multi-Input Architectures based on CNN Models using Real and Synthetic Images |
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2024 |
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Accepted in 27th The Iberomican Congress on Pattern Recognition CIARP 2024 |
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251 |
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Dennys Paillacho Chiluiza & Steven Silva Mendoza |
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Exploring the Perceptions and Challenges of Social Robot Navigation: Two Case Studies in Different Socio-Technical Contexts |
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2024 |
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Accepted in 36th Australian Conference on Human-Computer Interaction |
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cidis @ cidis @ |
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252 |
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Patricia Suarez Riofrio & Angel D. Sappa |
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Title |
Thermal Image Synthesis: Bridging the Gap between Visible and Infrared Spectrum |
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2024 |
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Accepted in 19th International Symposium on Visual Computing 2024 |
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cidis @ cidis @ |
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253 |
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Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco |
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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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Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa |
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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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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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Author |
Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |
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Fine-tuning deep convolutional networks for lepidopterous genus recognition |
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2017 |
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Lecture Notes in Computer Science |
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Vol. 10125 LNCS |
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pp. 467-475 |
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gtsi @ user @ |
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63 |
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Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo |
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Cross-Spectral Local Descriptors via Quadruplet Network |
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2017 |
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In Sensors Journal |
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Vol. 17 |
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pp. 873 |
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64 |
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Victor Santos; Angel D. Sappa; Miguel Oliveira |
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Special Issue on Autonomous Driving an Driver Assistance Systems |
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2017 |
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In Robotics and Autonomous Systems Journal |
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Vol. 91 |
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pp. 208-209 |
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