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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 | Morocho-Cayamcela, M.E. & W. Lim | ||||
| Title | Lateral confinement of high-impedance surface-waves through reinforcement learning | Type | Journal Article | ||
| Year |
2020 | Publication | Electronics Letters | Abbreviated Journal | |
| Volume | Vol. 56 | Issue | 23, 12 November 2020 | Pages | pp. 1262-1264 |
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| Abstract | The authors present a model-free policy-based reinforcement learning model that introduces perturbations on the pattern of a metasurface. The objective is to learn a policy that changes the size of the patches, and therefore the impedance in the sides of an artificially structured material. The proposed iterative model assigns the highest reward when the patch sizes allow the transmission along a constrained path and penalties when the patch sizes make the surface wave radiate to the sides of the metamaterial. After convergence, the proposed model learns an optimal patch pattern that achieves lateral confinement along the metasurface. Simulation results show that the proposed learned-pattern can effectively guide the electromagnetic wave through a metasurface, maintaining its instantaneous eigenstate when the homogeneity is perturbed. Moreover, the pattern learned to prevent reflections by changing the patch sizes adiabatically. The reflection coefficient S1, 2 shows that most of the power gets transferred from the source to the destination with the proposed design. |
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| Language | English | Summary Language | Original Title | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 139 | ||
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| Author | Morocho-Cayamcela, M.E. | ||||
| Title | Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling | Type | Journal Article | ||
| Year |
2020 | Publication | Electronic Letters on Computer Vision and Image Analysis (ELCVIA) | Abbreviated Journal | |
| Volume | Vol. 19 | Issue | Issue 2 | Pages | pp. 17-21 |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 140 | ||
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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 | ||
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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 | 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 | ||
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2020 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 615-625 | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 145 | ||
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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 | Viñán-Ludeña M.S., De Campos L.M., Roberto Jacome Galarza, & Sinche Freire, J. | ||||
| Title | Social media influence: a comprehensive review in general and in tourism domain | Type | Journal Article | ||
| Year |
2020 | Publication | Smart Innovation, Systems and Technologies. | Abbreviated Journal | |
| Volume | 171, 2020 | Issue | Pages | 25-35 | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 190 | ||
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| Author | Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco | ||||
| Title | Computer vision for image understanding. A comprehensive review | Type | Conference Article | ||
| Year |
2019 | Publication | International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador | Abbreviated Journal | |
| Volume | Issue | Pages | 248-259 | ||
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| Abstract | Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction. | ||||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 97 | ||
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| Author | Marjorie Chalen; Boris X. Vintimilla | ||||
| Title | Towards Action Prediction Applying Deep Learning | Type | Journal Article | ||
| Year |
2019 | Publication | Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 | Abbreviated Journal | |
| Volume | Issue | Pages | pp. 1-3 | ||
| Keywords | action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm. | ||||
| Abstract | Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task. | ||||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 129 | ||
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