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Author |
Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa |
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Title |
Corn Kernel Classification From Few Training Samples |
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2023 |
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In journal Artificial Intelligence in Agriculture |
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Vol. 9 |
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pp. 89-99 |
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cidis @ cidis @ |
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223 |
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Author |
Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa |
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Title |
Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach |
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2023 |
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17th International Conference On Signal Image Technology & Internet Based System |
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cidis @ cidis @ |
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224 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa |
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Title |
Boosting Guided Super-Resolution Performance with Synthesized Images |
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Conference Article |
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2023 |
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17th International Conference On Signal Image Technology & Internet Based Systems |
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cidis @ cidis @ |
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225 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa |
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Title |
Depth Map Estimation from a Single 2D Image |
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2023 |
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17th International Conference On Signal Image Technology & Internet Based Systems |
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cidis @ cidis @ |
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226 |
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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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Year |
2023 |
Publication |
IEEE access |
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Vol. 11 |
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Pages |
121457 - 121469 |
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21693536 |
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no |
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cidis @ cidis @ |
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227 |
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Sara Nieto, Evelyn Mejia, Ricardo Villacis, Fernanda Calderon, Hector Villegas, Jonathan Paillacho and Miguel Realpe |
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Title |
A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning |
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Conference Article |
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Year |
2023 |
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International Conference on Geospatial Information Sciences, iGISc 2023 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
228 |
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Author |
Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa |
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Title |
Tiny and Efficient Model for the Edge Detection Generalization |
Type |
Conference Article |
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Year |
2023 |
Publication |
Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 |
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Pages |
1356 - 1365 |
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no |
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Call Number |
cidis @ cidis @ |
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229 |
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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. |
Type |
Journal Article |
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Year |
2020 |
Publication |
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Abbreviated Journal |
In Sensors |
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Volume |
Vol. 2020-August |
Issue |
16 |
Pages |
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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English |
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ISBN |
14248220 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
133 |
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Author |
Xavier Soria; Edgar Riba; Angel D. Sappa |
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Title |
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection |
Type |
Conference Article |
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Year |
2020 |
Publication |
2020 IEEE Winter Conference on Applications of Computer Vision (WACV) |
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Issue |
9093290 |
Pages |
1912-1921 |
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Abstract |
This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered. |
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978-172816553-0 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
126 |
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Author |
Henry O. Velesaca, Steven Araujo, Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa |
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Title |
Off-the-Shelf Based System for Urban Environment Video Analytics. |
Type |
Conference Article |
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Year |
2020 |
Publication |
The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) |
Abbreviated Journal |
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2020-July |
Issue |
9145121 |
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459-464 |
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Greenhouse gases, carbon footprint, object detection, object tracking, website framework, off-the-shelf video analytics. |
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Abstract |
This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary
information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to
evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach. |
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21578672 |
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978-172817539-3 |
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Call Number |
cidis @ cidis @ |
Serial |
125 |
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