| Records |
| Author |
Jácome Galarza, Luis Roberto |
| Title |
Estimation of Corn Crop Yield using Multimodal Deep Learning from Multispectral Images and Environmental Sensors |
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Conference Article |
| Year |
2024 |
Publication |
19ª Conferência Ibérica de Sistemas e Tecnologias de Informação; CISTI'2024 |
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no |
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cidis @ cidis @ |
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246 |
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| Author |
Patricia L. Suarez, Dario Carpio, Angel D. Sappa |
| Title |
Enhancement of Guided Thermal Image Super-Resolution Approaches |
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| Year |
2024 |
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Neurocomputing |
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573 |
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Neurocomputing |
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no |
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cidis @ cidis @ |
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247 |
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| Author |
Henry O. Velesaca, Gisel Bastidas, Mohammad Rouhani, Angel D. Sappa |
| Title |
Multimodal image registration techniques: a comprehensive survey |
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| Year |
2024 |
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Multimedia Tools and Applications |
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Vol. 83 |
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63919 - 63947 |
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13807501 |
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no |
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cidis @ cidis @ |
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248 |
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| Author |
Leo Ramos & Angel D. Sappa |
| Title |
Multispectral Semantic Segmentation for Land Cover Classification: An Overview |
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| Year |
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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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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no |
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cidis @ cidis @ |
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251 |
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| Author |
Dennys Paillacho Chiluiza & Steven Silva Mendoza |
| Title |
Exploring the Perceptions and Challenges of Social Robot Navigation: Two Case Studies in Different Socio-Technical Contexts |
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Conference Article |
| Year |
2024 |
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Accepted in 36th Australian Conference on Human-Computer Interaction |
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no |
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cidis @ cidis @ |
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252 |
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| Author |
Patricia Suarez Riofrio & Angel D. Sappa |
| Title |
Thermal Image Synthesis: Bridging the Gap between Visible and Infrared Spectrum |
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Conference Article |
| Year |
2024 |
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Accepted in 19th International Symposium on Visual Computing 2024 |
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no |
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cidis @ cidis @ |
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253 |
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| Author |
Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez |
| Title |
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. |
Type |
Journal Article |
| Year |
2020 |
Publication |
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In Sensors |
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Vol. 2020-August |
Issue |
16 |
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pp. 1-23 |
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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 |
| 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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14248220 |
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no |
| Call Number |
cidis @ cidis @ |
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133 |
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| Author |
Xavier Soria; Edgar Riba; Angel D. Sappa |
| Title |
Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection |
Type |
Conference Article |
| Year |
2020 |
Publication |
2020 IEEE Winter Conference on Applications of Computer Vision (WACV) |
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| Volume |
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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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Approved |
no |
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cidis @ cidis @ |
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126 |
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| Author |
Henry O. Velesaca, Steven Araujo, Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa |
| Title |
Off-the-Shelf Based System for Urban Environment Video Analytics. |
Type |
Conference Article |
| Year |
2020 |
Publication |
The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) |
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2020-July |
Issue |
9145121 |
Pages |
459-464 |
| Keywords |
Greenhouse gases, carbon footprint, object detection, object tracking, website framework, off-the-shelf video analytics. |
| 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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English |
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21578672 |
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978-172817539-3 |
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no |
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cidis @ cidis @ |
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125 |
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