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Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic | ||||
| Title | A Fault Tolerant Perception system for autonomous vehicles | Type | Conference Article | ||
| Year | 2016 | Publication | 35th Chinese Control Conference (CCC2016), International Conference on, Chengdu | Abbreviated Journal | |
| Volume | Issue | Pages | 1-6 | ||
| Keywords | Fault Tolerant Perception, Sensor Data Fusion, Fault Tolerance, Autonomous Vehicles, Federated Architecture | ||||
| Abstract | Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset. | ||||
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| Language | English | Summary Language | English | Original Title | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 52 | ||
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Mildred Cruz; Cristhian A. Aguilera; Boris X. Vintimilla; Ricardo Toledo; Ángel D. Sappa | ||||
| Title | Cross-spectral image registration and fusion: an evaluation study | Type | Conference Article | ||
| Year | 2015 | Publication | 2nd International Conference on Machine Vision and Machine Learning | Abbreviated Journal | |
| Volume | 331 | Issue | Pages | ||
| Keywords | multispectral imaging; image registration; data fusion; infrared and visible spectra | ||||
| Abstract | This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. | ||||
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| Publisher | Computer Vision Center | Place of Publication | Barcelona, Spain | Editor | |
| Language | English | Summary Language | English | Original Title | |
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 35 | ||
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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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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 87 | ||
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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 |
Omar Coello, Moisés Coronel, Darío Carpio, Boris X. Vintimilla & Luis Chuquimarca | ||||
| Title | Enhancing Apple’s Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging | Type | Conference Article | ||
| Year | 2024 | Publication | 14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024 | Abbreviated Journal | |
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| ISSN | ISBN | 979-835037565-7 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | cidis @ cidis @ | Serial | 244 | ||
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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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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 |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture | Type | Conference Article | ||
| Year | 2017 | Publication | 19th International Conference on Image Analysis and Processing. | Abbreviated Journal | |
| Volume | Issue | Pages | 287-297 | ||
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 66 | ||
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Learning Image Vegetation Index through a Conditional Generative Adversarial Network | Type | Conference Article | ||
| Year | 2017 | Publication | 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) | Abbreviated Journal | |
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| Call Number | gtsi @ user @ | Serial | 70 | ||
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| Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla | ||||
| Title | Vegetation Index Estimation from Monospectral Images | Type | Conference Article | ||
| Year | 2018 | Publication | 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science | Abbreviated Journal | |
| Volume | 10882 | Issue | Pages | 353-362 | |
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| Abstract | This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance details, resulting in a sharp NDVI image. Then, the discriminative model estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
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| Notes | Approved | no | |||
| Call Number | gtsi @ user @ | Serial | 82 | ||
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