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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 | ||
| 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 | ||
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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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| Area | Expedition | Conference | |||
| 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. | ||||
| 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 | ||
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| 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 | ||||
| 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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 115 | ||
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| Author | Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca | ||||
| Title | Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem | Type | Conference Article | ||
| Year | 2020 | Publication | The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 | Abbreviated Journal | |
| Volume | 4 | Issue |
Pages | 498-505 | |
| Keywords | Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters. | ||||
| Abstract | This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consist of first training the network using pairs of images from the virtual-world scenario considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on the training due to the reduced number of pairs of real-images on most of the public data sets. |
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| ISSN | ISBN | 978-989758402-2 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 120 | ||
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| Author | Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Thermal Image Super-Resolution: a Novel Architecture and Dataset | Type | Conference Article | ||
| Year | 2020 | Publication | The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020 | Abbreviated Journal | |
| Volume | 4 | Issue |
Pages | 111-119 | |
| Keywords | Thermal images, Far Infrared, Dataset, Super-Resolution. | ||||
| Abstract | This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem. The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available. |
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| ISSN | ISBN | 978-989758402-2 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 121 | ||
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| Author | Nayeth I. Solorzano Alcivar, Robert Loor, Stalyn Gonzabay Yagual, & Boris X. Vintimilla | ||||
| 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 |
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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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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 131 | ||
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| Author | Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla | ||||
| 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 | ||||
| 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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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 181 | ||
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| Author | Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla | ||||
| 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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| Author | Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Jin Kim, Dogun Kim et al. | ||||
| Title | Thermal Image Super-Resolution Challenge Results- PBVS 2022. | Type | Conference Article | ||
| Year | 2022 | Publication | Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. | Abbreviated Journal | CONFERENCE |
| Volume | 2022-June | Issue |
Pages | 349-357 | |
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| Abstract | This paper presents results from the third Thermal Image Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop. The challenge uses the same thermal image dataset as the first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was kept aside for testing. The evaluation tasks were to measure the PSNR and SSIM between the SR image and the ground truth (HR thermal noisy image downsampled by four), and also to measure the PSNR and SSIM between the SR image and the semi-registered HR image (acquired with another camera). The results outperformed those from last year’s challenge, improving both evaluation metrics. This year, almost 100 teams participants registered for the challenge, showing the community’s interest in this hot topic. |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 175 | ||
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