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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 | ||
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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 | ||
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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 | ||
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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 | ||
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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 | ||
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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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| Author | Armin Mehri; Angel D. Sappa | ||||
| Title | Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples | Type | Conference Article | ||
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2019 | Publication | Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States | Abbreviated Journal | |
| Volume | Issue | Pages | 971-979 | ||
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| Abstract | This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation metrics—and qualitatively evaluated showing considerable improvements with respect to the state of the art |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 105 | ||
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| Author | Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Image Vegetation Index through a Cycle Generative Adversarial Network | Type | Conference Article | ||
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2019 | Publication | Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States | Abbreviated Journal | |
| Volume | Issue | Pages | 1014-1021 | ||
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| Abstract | This paper proposes a novel approach to estimate the Normalized Difference Vegetation Index (NDVI) just from an RGB image. The NDVI values are obtained by using images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The cycled GAN network is able to obtain a NIR image from a given gray scale image. It is trained by using unpaired set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are obtained from the provided RGB images). Then, the NIR image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous approaches are also provided. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 106 | ||
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| Author | Angel Morera; Angel Sánchez; Angel D. Sappa; José F. Vélez | ||||
| Title | Robust Detection of Outdoor Urban Advertising Panels in Static Images. | Type | Conference Article | ||
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2019 | Publication | 17th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2019); Ávila, España. Communications in Computer and Information Science | Abbreviated Journal | |
| Volume | 1047 | Issue | Pages | 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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 107 | ||
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