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| Author | Patricia L. Suarez | ||||
| Title | Procesamiento y representación de imágenes multiespectrales usando técnicas de aprendizaje profundo (Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector.). Ph.D. thesis. | Type | Book Chapter | ||
| Year | 2020 | Publication  |
Ediciones FIEC-ESPOL. | Abbreviated Journal | |
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| Corporate Author | Ph.D. Angel Sappa, Director & Ph.D. Boris Vintimilla, Codirector. | Thesis | |||
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| Language | Español | Summary Language | Original Title | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 144 | ||
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| Author | Viñán-Ludeña, M.S., Roberto Jacome Galarza, Montoya, L.R., Leon, A.V., & Ramírez, C.C. | ||||
| Title | Smart university: an architecture proposal for information management using open data for research projects. | Type | Journal Article | ||
| Year | 2020 | Publication |
Advances in Intelligent Systems and Computing | Abbreviated Journal | |
| Volume | 1137 AISC, 2020 | Issue | Pages | 172-178 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 188 | ||
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| Author | Juca Aulestia M., Labanda Jaramillo M., Guaman Quinche J., Coronel Romero E., Chamba Eras L., & Roberto Jacome Galarza | ||||
| Title | Open innovation at university: a systematic literature review | Type | Journal Article | ||
| Year | 2020 | Publication |
Advances in Intelligent Systems and Computing | Abbreviated Journal | |
| Volume | 1159 AISC, 2020 | Issue | Pages | 3-14 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 189 | ||
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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) | Abbreviated Journal | |
| Volume | 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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| ISSN | ISBN | 978-172816553-0 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 126 | ||
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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 |
Abbreviated Journal | In Sensors | |
| Volume | Vol. 2020-August | Issue | 16 | Pages | pp. 1-23 |
| 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 | ||||
| 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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| Language | English | Summary Language | English | Original Title | |
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| ISSN | ISBN | 14248220 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 133 | ||
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| Author | Rosero Vasquez Shendry | ||||
| Title | Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020. | Type | Journal Article | ||
| Year | 2020 | Publication |
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| Volume | Issue | Pages | 615-625 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 145 | ||
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