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Author Jorge L. Charco; Boris X. Vintimilla; Angel D. Sappa pdf  openurl
  Title Deep learning based camera pose estimation in multi-view environment. Type Conference Article
  Year 2018 Publication 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) Abbreviated Journal  
  Volume Issue Pages 224-228  
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  Abstract This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from scratch on a large data set that takes as input a pair of images from the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose.  
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  Call Number gtsi @ user @ Serial 93  
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Author Marjorie Chalen; Boris X. Vintimilla pdf  openurl
  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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  Call Number cidis @ cidis @ Serial 129  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud pdf  openurl
  Title Image Vegetation Index through a Cycle Generative Adversarial Network Type Conference Article
  Year 2019 Publication Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States Abbreviated Journal  
  Volume Issue Pages 1014-1021  
  Keywords  
  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 Rafael E. Rivadeneira; Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla. pdf  openurl
  Title Thermal Image SuperResolution through Deep Convolutional Neural Network. Type Conference Article
  Year 2019 Publication 16th International Conference on Image Analysis and Recognition (ICIAR 2019); Waterloo, Canadá Abbreviated Journal  
  Volume Issue Pages 417-426  
  Keywords  
  Abstract Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a superesolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process a new thermal images dataset is used. Di?erent experiments have been carried out, ?rstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset  
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  Notes Approved no  
  Call Number gtsi @ user @ Serial 103  
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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla pdf  openurl
  Title Image patch similarity through a meta-learning metric based approach Type Conference Article
  Year 2019 Publication 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia Abbreviated Journal  
  Volume Issue Pages 511-517  
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  Abstract Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results

have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results.		  
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  Call Number gtsi @ user @ Serial 115  
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Author Nayeth I. Solorzano Alcivar, Robert Loor, Stalyn Gonzabay Yagual, & Boris X. Vintimilla pdf  openurl
  Title Statistical Representations of a Dashboard to Monitor Educational Videogames in Natural Language Type Conference Article
  Year 2020 Publication ETLTC – ACM Chapter: International Conference on Educational Technology, Language and Technical Communication; Fukushima, Japan, 27-31 Enero 2020 Abbreviated Journal  
  Volume 77 Issue Pages  
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  Abstract This paper explains how Natural Language (NL) processing by computers, through smart

programs as a way of Machine Learning (ML), can represent large sets of quantitative data as written

statements. The study recognized the need to improve the implemented web platform using a

dashboard in which we collected a set of extensive data to measure assessment factors of using

children´s educational games. In this case, applying NL is a strategy to give assessments, build, and

display more precise written statements to enhance the understanding of children´s gaming behavior.

We propose the development of a new tool to assess the use of written explanations rather than a

statistical representation of feedback information for the comprehension of parents and teachers with

a lack of primary level knowledge in statistics. Applying fuzzy logic theory, we present verbatim

explanations of children´s behavior playing educational videogames as NL interpretation instead of

statistical representations. An educational series of digital game applications for mobile devices,

identified as MIDI (Spanish acronym of “Interactive Didactic Multimedia for Children”) linked to a

dashboard in the cloud, is evaluated using the dashboard metrics. MIDI games tested in local primary

schools helps to evaluate the results of using the proposed tool. The guiding results allow analyzing

the degrees of playability and usability factors obtained from the data produced when children play a

MIDI game. The results obtained are presented in a comprehensive guiding evaluation report

applying NL for parents and teachers. These guiding evaluations are useful to enhance children's

learning understanding related to the school curricula applied to ludic digital games.		  
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  Call Number cidis @ cidis @ Serial 131  
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Author Patricia L. Suárez, Angel D. Sappa and Boris X. Vintimilla url  openurl
  Title Deep learning-based vegetation index estimation Type Book Chapter
  Year 2021 Publication Generative Adversarial Networks for Image-to-Image Translation Book. Abbreviated Journal  
  Volume Chapter 9 Issue Issue 2 Pages 205-232  
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  Call Number cidis @ cidis @ Serial 137  
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Author Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla pdf  openurl
  Title Cycle generative adversarial network: towards a low-cost vegetation index estimation Type Conference Article
  Year 2021 Publication IEEE International Conference on Image Processing (ICIP 2021) Abbreviated Journal  
  Volume 2021-September Issue Pages 2783-2787  
  Keywords CyclicGAN, NDVI, near infrared spectra, instance normalization.  
  Abstract This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 164  
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Author Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla pdf  openurl
  Title Multi-Image Super-Resolution for Thermal Images. Type Conference Article
  Year 2022 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 Abbreviated Journal  
  Volume 4 Issue Pages 635 - 642  
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  Call Number cidis @ cidis @ Serial 181  
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Author Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla pdf  openurl
  Title Human Pose Estimation through A Novel Multi-View Scheme Type Conference Article
  Year 2022 Publication Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022 Abbreviated Journal  
  Volume 5 Issue Pages 855-862  
  Keywords Multi-View Scheme, Human Pose Estimation, Relative Camera Pose, Monocular Approach  
  Abstract This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human

pose estimation problem. The proposed approach first obtains the human body joints of a set of images,

which are captured from different views at the same time. Then, it enhances the obtained joints by using a

multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from

another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed

for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and

comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements

in the accuracy of body joints estimations.		  
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  Notes Approved yes  
  Call Number cidis @ cidis @ Serial 169  
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