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Miguel Realpe, Boris X. Vintimilla, & Ljubo Vlacic. (2016). A Fault Tolerant Perception system for autonomous vehicles. In 35th Chinese Control Conference (CCC2016), International Conference on, Chengdu (pp. 1–6).
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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Mildred Cruz, Cristhian A. Aguilera, Boris X. Vintimilla, Ricardo Toledo, & Ángel D. Sappa. (2015). Cross-spectral image registration and fusion: an evaluation study. In 2nd International Conference on Machine Vision and Machine Learning (Vol. 331). Barcelona, Spain: Computer Vision Center.
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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Milton Mendieta, F. Panchana, B. Andrade, B. Bayot, C. Vaca, Boris X. Vintimilla, et al. (2018). Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. In IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador (pp. 1–6).
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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Nayeth I. Solorzano Alcivar, R. L., Stalyn Gonzabay Yagual, & Boris X. Vintimilla. (2020). Statistical Representations of a Dashboard to Monitor Educational Videogames in Natural Language. In ETLTC – ACM Chapter: International Conference on Educational Technology, Language and Technical Communication; Fukushima, Japan, 27-31 Enero 2020 (Vol. 77).
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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Omar Coello, M. C., Darío Carpio, Boris X. Vintimilla & Luis Chuquimarca. (2024). Enhancing Apple’s Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging. In 14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024.
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Patricia L. Suárez, A. D. S. and B. X. V. (2021). Deep learning-based vegetation index estimation. In Generative Adversarial Networks for Image-to-Image Translation Book. (Vol. Chapter 9, pp. 205–232).
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Patricia L. Suárez, A. D. S., Boris X. Vintimilla. (2021). Cycle generative adversarial network: towards a low-cost vegetation index estimation. In IEEE International Conference on Image Processing (ICIP 2021) (Vol. 2021-September, pp. 2783–2787).
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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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture. In 19th International Conference on Image Analysis and Processing. (pp. 287–297).
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Learning Image Vegetation Index through a Conditional Generative Adversarial Network. In 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM).
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2018). Vegetation Index Estimation from Monospectral Images. In 15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science (Vol. 10882, pp. 353–362).
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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