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| Author | Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Chenyang Wang, Junjun Jiang, Xianming Liu, Zhiwei Zhong, Dai Bin, Li Ruodi, Li Shengye | ||||
| Title | Thermal Image Super-Resolution Challenge Results – PBVS 2023 | Type | Conference Article | ||
| Year | 2023 | Publication | 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 | Abbreviated Journal | |
| Volume | 2023-June | Issue | Pages  |
470 - 478 | |
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| ISSN | 21607508 | ISBN | 979-835030249-3 | Medium | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 210 | ||
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| Author | Rivadeneira, Rafael E.; Sappa, Angel D. and Vintimilla Boris X. | ||||
| Title | Thermal Image Super-Resolution: A Novel Unsupervised Approach. | Type | Book Chapter | ||
| Year | 2022 | Publication | Communications in Computer and Information Science, 15th International Communications in Computer and Information Science Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications | Abbreviated Journal | BOOK |
| Volume | 1474 | Issue | Pages |
495-506 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 179 | ||
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| Author | Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa | ||||
| Title | A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells. | Type | Journal Article | ||
| Year | 2022 | Publication | Journal of Manufacturing Systems | Abbreviated Journal | |
| Volume | Vol. 64 | Issue | Pages |
pp. 497-507 | |
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| Call Number | cidis @ cidis @ | Serial | 184 | ||
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| Author | Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca | ||||
| Title | Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem | Type | Conference Article | ||
| Year | 2020 | Publication | The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 | Abbreviated Journal | |
| Volume | 4 | Issue | Pages |
498-505 | |
| Keywords | Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters. | ||||
| Abstract | This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consist of first training the network using pairs of images from the virtual-world scenario considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on the training due to the reduced number of pairs of real-images on most of the public data sets. |
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| ISSN | ISBN | 978-989758402-2 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 120 | ||
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| Author | Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias | ||||
| Title | Scene representations for autonomous driving: an approach based on polygonal primitives | Type | Conference Article | ||
| Year | 2015 | Publication | Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 | Abbreviated Journal | |
| Volume | 417 | Issue | Pages |
503-515 | |
| Keywords | Scene reconstruction, Point cloud, Autonomous vehicles | ||||
| Abstract | In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. | ||||
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| Publisher | Springer International Publishing Switzerland 2016 | Place of Publication | Editor | ||
| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | Second Iberian Robotics Conference | ||
| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 45 | ||
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| Author | Jacome-Galarza L.-R., Realpe Robalino M.-A., Paillacho Corredores J., Benavides Maldonado J.-L. | ||||
| Title | Time series in sensor data using state of the art deep learning approaches: A systematic literature review. | Type | Conference Article | ||
| Year | 2022 | Publication | VII International Conference on Science, Technology and Innovation for Society (CITIS 2021), mayo 26-28. Smart Innovation, Systems and Technologies. | Abbreviated Journal | |
| Volume | 252 | Issue | Pages |
503-514 | |
| Keywords | time series, deep learning, recurrent networks, sensor data, IoT. | ||||
| Abstract | IoT (Internet of Things) and AI (Artificial Intelligence) are becoming support tools for several current technological solutions due to significant advancements of these areas. The development of the IoT in various technological fields has contributed to predicting the behavior of various systems such as mechanical, electronic, and control using sensor networks. On the other hand, deep learning architectures have achieved excellent results in complex tasks, where patterns have been extracted in time series. This study has reviewed the most efficient deep learning architectures for forecasting and obtaining trends over time, together with data produced by IoT sensors. In this way, it is proposed to contribute to applications in fields in which IoT is contributing a technological advance such as smart cities, industry 4.0, sustainable agriculture, or robotics. Among the architectures studied in this article related to the process of time series data we have: LSTM (Long Short-Term Memory) for its high precision in prediction and the ability to automatically process input sequences; CNN (Convolutional Neural Networks) mainly in human activity recognition; hybrid architectures in which there is a convolutional layer for data pre-processing and RNN (Recurrent Neural Networks) for data fusion from different sensors and their subsequent classification; and stacked LSTM Autoencoders that extract the variables from time series in an unsupervised way without the need of manual data pre-processing.Finally, well-known technologies in natural language processing are also used in time series data prediction, such as the attention mechanism and embeddings obtaining promising results. |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 152 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Image patch similarity through a meta-learning metric based approach | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia | Abbreviated Journal | |
| Volume | Issue | Pages |
511-517 | ||
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| Abstract | Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 115 | ||
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| Author | Spencer Low, Oliver Nina, Angel D. Sappa, Erik Blasch, Nathan Inkawhich | ||||
| Title | Multi-modal Aerial View Image Challenge: Translation from Synthetic Aperture Radar to Electro-Optical Domain Results – PBVS 2023 | Type | Conference Article | ||
| Year | 2023 | Publication | 19th IEEE Workshop on Perception Beyond the Visible Spectrum de la Conferencia Computer Vision & Pattern Recognition CVPR 2023, junio 18-28 | Abbreviated Journal | |
| Volume | 2023-June | Issue | Pages |
515 - 523 | |
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| ISSN | 21607508 | ISBN | 979-835030249-3 | Medium | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 211 | ||
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| Author | Julien Poujol; Cristhian A. Aguilera; Etienne Danos; Boris X. Vintimilla; Ricardo Toledo; Angel D. Sappa | ||||
| Title | A visible-Thermal Fusion based Monocular Visual Odometry | Type | Conference Article | ||
| Year | 2015 | Publication | Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 | Abbreviated Journal | |
| Volume | 417 | Issue | Pages |
517-528 | |
| Keywords | Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion | ||||
| Abstract | The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. | ||||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 44 | ||
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| Author | Luis Chuquimarca, Boris Vintimilla & Sergio Velastin | ||||
| Title | Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets. | 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 |
536 - 543 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 202 | ||
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