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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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Patricia L. Suarez, Angel D. Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2018). Near InfraRed Imagery Colorization. In 25 th IEEE International Conference on Image Processing, ICIP 2018 (pp. 2237–2241).
Abstract: This paper proposes a stacked conditional Generative
Adversarial Network-based method for Near InfraRed
(NIR) imagery colorization. We propose a variant architecture
of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model.
We show that this new architecture/loss-function yields better
generalization and representation of the generated colored IR
images. The proposed approach is evaluated on a large test
dataset and compared to recent state of the art methods using
standard metrics.1
Index Terms—Convolutional Neural Networks (CNN), Generative
Adversarial Network (GAN), Infrared Imagery colorization.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2018). Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images. In International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing (Vol. 721).
Abstract: This paper proposes a novel approach to use cross-spectral
images to achieve a better performance with the proposed Adaptive Harris
corner detector comparing its obtained results with those achieved
with images of the visible spectra. The images of urban, field, old-building
and country category were used for the experiments, given the variety of
the textures present in these images, with which the complexity of the
proposal is much more challenging for its verification. It is a new scope,
which means improving the detection of characteristic points using crossspectral
images (NIR, G, B) and applying pruning techniques, the combination
of channels for this fusion is the one that generates the largest
variance based on the intensity of the merged pixels, therefore, it is that
which maximizes the entropy in the resulting Cross-spectral images.
Harris is one of the most widely used corner detection algorithm, so
any improvement in its efficiency is an important contribution in the
field of computer vision. The experiments conclude that the inclusion of
a (NIR) channel in the image as a result of the combination of the spectra,
greatly improves the corner detection due to better entropy of the
resulting image after the fusion, Therefore the fusion process applied to
the images improves the results obtained in subsequent processes such as
identification of objects or patterns, classification and/or segmentation.
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Wilton Agila, Gomer Rubio, L. Miranda, & L. Vázquez. (2018). Qualitative Model of Control in the Pressure Stabilization of PEM Fuel Cell. In 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. (pp. 1221–1226).
Abstract: This work describes an approximate reasoning
technique to deal with the non-linearity that occurs in the
stabilization of the pressure of anodic and cathodic gases of a
proton exchange membrane fuel cell (PEM). The implementation
of a supervisory element in the stabilization of the pressure of the
PEM cell is described. The fuzzy supervisor is a reference
control, it varies the value of the reference given to the classic
low-level controller, Proportional – Integral – Derivative (PID),
according to the speed of change of the measured pressure and
the change in the error of the pressure. The objective of the fuzzy
supervisor is to achieve a rapid response over time of the variable
pressure, avoiding unwanted overruns with respect to the
reference value. A comparative analysis is detailed with the
classic PID control to evaluate the operation of the "fuzzy
supervisor", with different flow values and different sizes of
active area of the PEM cell (electric power generated).
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Gomer Rubio, & Wilton Agila. (2018). Dynamic Modeling of Fuel Cells in a Strategic Context. In 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia..
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Del Pino, J., Salazar, G., & Cedeño, V. M. (2011). Adaptación de un Recomendador de Filtro Colaborativo Basado en el Usuario para la Creación de un Recomendador de Materias de Pregrado Basado en el Historial Académico de los Estudiantes. Revista Tecnológica ESPOL, Vol. 24, pp. 29–34.
Abstract: Los sistemas de recomendación son ampliamente utilizados hoy en día gracias a su capacidad de analizar las preferencias de usuarios y sugerir ítems. No obstante, el uso de los recomendadores está limitado a un modelo basado en el usuario y no en su historial de preferencias, discriminando así el campo de aplicación, por ejemplo, a sistemas académicos donde sea primordial el estudio de las decisiones del estudiante a lo largo de su carrera. El presente
trabajo presenta un esfuerzo por adaptar filtros colaborativos basados en el usuario a filtros colaborativos basados en el historial del usuario. Con un conjunto de pruebas mediremos su efectividad utilizando dos algoritmos distintos de similaridad para recomendar materias a un estudiante en el sexto semestre de la carrera de Ingeniería en Electrónica y Telecomunicaciones ofertada por la FIEC – ESPOL. Los resultados muestran que es factible adaptar un recomendador a un modelo basado en el historial del usuario
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Carlos Monsalve, & Alain April and Alain Abran. (2011). Measuring software functional size from business process models. International Journal of Software Engineering and Knowledge Engineering, Vol. 21, pp. 311–338.
Abstract: ISO 14143-1 specifies that a functional size measurement (FSM) method must provide measurement procedures to quantify the functional user requirements (FURs) of software. Such quantitative information, functional size, is typically used, for instance, in software estimation. One of the international standards for FSM is the COSMIC FSM method — ISO 19761 — which was designed to be applied both to the business application (BA) software domain and to the real-time software domain. A recurrent problem in FSM is the availability and quality of the inputs required for measurement purposes; that is, well documented FURs. Business process (BP) models, as they are commonly used to gather requirements from the early stages of a project, could be a valuable source of information for FSM. In a previous article, the feasibility of such an approach for the BA domain was analyzed using the Qualigram BP modeling notation. This paper complements that work by: (1) analyzing the use of BPMN for FSM in the BA domain; (2) presenting notation-independent guidelines for the BA domain; and (3) analyzing the possibility of using BP models to perform FSM in the real-time domain. The measurement results obtained from BP models are compared with those of previous FSM case studies.
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Xavier Soria, & Angel D. Sappa. (2018). Improving Edge Detection in RGB Images by Adding NIR Channel. In 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) (pp. 266–273).
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Xavier Soria, Angel D. Sappa, & Riad Hammoud. (2018). Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Image. Sensors 2018 ,2059.Vol. 18(Issue 7).
Abstract: Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm). This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2018). Cross-spectral image dehaze through a dense stacked conditional GAN based approach. In 14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) (pp. 358–364).
Abstract: This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results.
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