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Author (down) 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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Author (down) 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 (down) 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 Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS) Abbreviated Journal
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Call Number cidis @ cidis @ Serial 244
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Author (down) 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 (down) 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 (down) 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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Call Number cidis @ cidis @ Serial 35
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Author (down) Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic
Title Sensor Fault Detection and Diagnosis for autonomous vehicles Type Conference Article
Year 2015 Publication 2nd International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2015), International Conference on, Singapur, 2015 Abbreviated Journal
Volume 30 Issue MATEC Web of Conferences Pages 1-6
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Abstract In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.
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Publisher EDP Sciences Place of Publication Editor
Language English Summary Language English Original Title
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Call Number cidis @ cidis @ Serial 42
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Author (down) Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic
Title Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles Type Journal Article
Year 2016 Publication Journal of Automation and Control Engineering (JOACE) Abbreviated Journal
Volume Vol. 4 Issue Pages pp. 430-436
Keywords Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System
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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Call Number cidis @ cidis @ Serial 51
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Author (down) 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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Call Number cidis @ cidis @ Serial 52
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Author (down) 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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