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Author | Dennis G. Romero; A. Frizera; Angel D. Sappa; Boris X. Vintimilla; T.F. Bastos | ||||
Title | A predictive model for human activity recognition by observing actions and context | Type | Conference Article | ||
Year | 2015 | Publication | ACIVS 2015 (Advanced Concepts for Intelligent Vision Systems), International Conference on, Catania, Italy, 2015 | Abbreviated Journal | |
Volume | Issue | Pages | 323 - 333 | ||
Keywords | Edge width, Image blu,r Defocus map, Edge model | ||||
Abstract | This paper presents a novel model to estimate human activities – a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach. | ||||
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Call Number | cidis @ cidis @ | Serial | 43 | ||
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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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Call Number | cidis @ cidis @ | Serial | 175 | ||
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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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Call Number | gtsi @ user @ | Serial | 106 | ||
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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 | 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 | ||||
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 | Julien Poujol; Cristhian A. Aguilera; Etienne Danos; Boris X. Vintimilla; Ricardo Toledo; Angel D. Sappa | ||||
Title | A visible-Thermal Fusion based Monocular Visual Odometry | Type | Conference Article | ||
Year | 2015 | Publication | Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 | Abbreviated Journal | |
Volume | 417 | Issue | Pages | 517-528 | |
Keywords | Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion | ||||
Abstract | The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. | ||||
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Language | English | Summary Language | English | Original Title | |
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Call Number | cidis @ cidis @ | Serial | 44 | ||
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Author | Jorge L. Charco, Angel D. Sappa, Boris X. Vintimilla, Henry O. Velesaca. | ||||
Title | Human Body Pose Estimation in Multi-view Environments. | Type | Book Chapter | ||
Year | 2022 | Publication | ICT Applications for Smart Cities Part of the Intelligent Systems Reference Library book series | Abbreviated Journal | BOOK |
Volume | 224 | Issue | Pages | 79-99 | |
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Call Number | cidis @ cidis @ | Serial | 197 | ||
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Author | Miguel Realpe; Boris X. Vintimilla; L. Vlacic | ||||
Title | Towards Fault Tolerant Perception for autonomous vehicles: Local Fusion. | Type | Conference Article | ||
Year | 2015 | Publication | IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, 2015. | Abbreviated Journal | |
Volume | Issue | Pages | 253-258 | ||
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Abstract | Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicle's environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system. | ||||
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Call Number | cidis @ cidis @ | Serial | 37 | ||
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Author | Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero | ||||
Title | Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. | Type | Conference Article | ||
Year | 2018 | Publication | IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador | Abbreviated Journal | |
Volume | Issue | Pages | 1-6 | ||
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Abstract | The identification of shrimp organs in biology using histological images is a complex task. Shrimp histological images poses a big challenge due to their texture and similarity among classes. Image classification by using feature engineering and convolutional neural networks (CNN) are suitable methods to assist biologists when performing organ detection. This work evaluates the Bag-of-Visual-Words (BOVW) and Pyramid-Bagof- Words (PBOW) models for image classification leveraging big data techniques; and transfer learning for the same classification task by using a pre-trained CNN. A comparative analysis of these two different techniques is performed, highlighting the characteristics of both approaches on the shrimp organs identification problem. |
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Call Number | gtsi @ user @ | Serial | 87 | ||
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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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Call Number | cidis @ cidis @ | Serial | 164 | ||
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