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Author |
P. Ricaurte; C. Chilán; C. A. Aguilera-Carrasco; B. X. Vintimilla; Angel D. Sappa |
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Title  |
Performance Evaluation of Feature Point Descriptors in the Infrared Domain |
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Conference Article |
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2014 |
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Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2013 |
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1 |
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545 -550 |
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Infrared Imaging, Feature Point Descriptors |
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This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered. |
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IEEE |
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English |
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2014 International Conference on Computer Vision Theory and Applications (VISAPP) |
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no |
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Call Number |
cidis @ cidis @ |
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26 |
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Author |
Raul A. Mira; Patricia L. Suarez; Rafael E. Rivadeneira; Angel D. Sappa |
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Title |
PETRA: A Crowdsourcing-Based Platform for Rocks Data Collection and Characterization |
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Conference Article |
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2019 |
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IEEE ETCM 2019 Fourth Ecuador Technical Chapters Meeting; Guayaquil, Ecuador |
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1-6 |
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This paper presents details of a distributed platform intended for data acquisition, evaluation, storage and visualization, which is fully implemented under the crowdsourcing paradigm. The proposed platform is the result from collaboration between computer science and petrology researchers and it is intended for academic purposes. The platform is designed within a MTV (Model, Template and View) architecture and also designed for a collaborative data store and managing of rocks from multiple readers and writers, taking advantage of ubiquity of web applications, and neutrality of researchers from different
communities to validate the data. The platform is being used and validated by students and academics from our university; in the near future it will be open to other users interested on this topic. |
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no |
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gtsi @ user @ |
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112 |
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Author |
Cristhian A. Aguilera; Xaver Soria; Angel D. Sappa; Ricardo Toledo |
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Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
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Conference Article |
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2017 |
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15th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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619 |
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155-163 |
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cidis @ cidis @ |
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59 |
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Author |
Angel Morera; Angel Sánchez; Angel D. Sappa; José F. Vélez |
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Title |
Robust Detection of Outdoor Urban Advertising Panels in Static Images. |
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Conference Article |
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2019 |
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17th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2019); Ávila, España. Communications in Computer and Information Science |
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1047 |
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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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no |
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gtsi @ user @ |
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107 |
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Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias |
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Title |
Scene representations for autonomous driving: an approach based on polygonal primitives |
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Conference Article |
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2015 |
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Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 |
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417 |
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503-515 |
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Keywords |
Scene reconstruction, Point cloud, Autonomous vehicles |
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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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Springer International Publishing Switzerland 2016 |
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English |
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English |
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Second Iberian Robotics Conference |
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no |
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Call Number |
cidis @ cidis @ |
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45 |
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Author |
Victor Santos; Angel D. Sappa; Miguel Oliveira |
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Title |
Special Issue on Autonomous Driving an Driver Assistance Systems |
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Journal Article |
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Year |
2017 |
Publication |
In Robotics and Autonomous Systems Journal |
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Vol. 91 |
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pp. 208-209 |
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no |
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gtsi @ user @ |
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65 |
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Author |
Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A. |
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Title |
Special Issue on Autonomous Driving and Driver Assistance Systems |
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Journal Article |
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2019 |
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In Robotics and Autonomous Systems |
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121 |
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no |
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Call Number |
gtsi @ user @ |
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119 |
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Author |
Armin Mehri, Parichehr Behjati, Dario Carpio, and Angel D. Sappa |
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Title |
SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution |
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Journal Article |
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2023 |
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IEEE access |
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Vol. 11 |
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121457 - 121469 |
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21693536 |
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Call Number |
cidis @ cidis @ |
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227 |
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Author |
Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez |
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Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. |
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Journal Article |
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2020 |
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In Sensors |
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Vol. 2020-August |
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16 |
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pp. 1-23 |
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Keywords |
object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities |
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Abstract |
This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO)
deep neural networks for the outdoor advertisement panel detection problem by handling multiple
and combined variabilities in the scenes. Publicity panel detection in images o ers important
advantages both in the real world as well as in the virtual one. For example, applications like Google
Street View can be used for Internet publicity and when detecting these ads panels in images, it could
be possible to replace the publicity appearing inside the panels by another from a funding company.
In our experiments, both SSD and YOLO detectors have produced acceptable results under variable
sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex
background and multiple panels in scenes. Due to the diculty of finding annotated images for the
considered problem, we created our own dataset for conducting the experiments. The major strength
of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable
when the publicity contained inside the panel is analyzed after detecting them. On the other side,
YOLO produced better panel localization results detecting a higher number of True Positive (TP)
panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models
with di erent types of semantic segmentation networks and using the same evaluation metrics is
also included. |
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14248220 |
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Call Number |
cidis @ cidis @ |
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133 |
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Author |
Henry O. Velesaca, Patricia L. Suarez, Dario Carpio, and Angel D. Sappa |
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Title |
Synthesized Image Datasets: Towards an Annotation-Free Instance Segmentation Strategy |
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Conference Article |
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2021 |
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16 International Symposium on Visual Computing. Octubre 4-6, 2021. Lecture Notes in Computer Science |
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13017 |
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131-143 |
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cidis @ cidis @ |
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163 |
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