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Author |
Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero |
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Title |
Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering. |
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Conference Article |
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2018 |
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IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador |
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1-6 |
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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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gtsi @ user @ |
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87 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Deep Learning based Single Image Dehazing |
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Conference Article |
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2018 |
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14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA |
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This paper proposes a novel approach to remove haze
degradations in RGB images using a stacked conditional
Generative Adversarial Network (GAN). It employs a triplet
of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a
conditional probabilistic model, is proposed. The proposed
GAN architecture learns to remove the haze, using as conditioned
entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast
model training convergence and a homogeneous model generalization.
Experiments showed that the proposed method
generates high-quality clear images. |
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gtsi @ user @ |
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83 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Vegetation Index Estimation from Monospectral Images |
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Conference Article |
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Year |
2018 |
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15th International Conference, Image Analysis and Recognition (ICIAR 2018), Póvoa de Varzim, Portugal. Lecture Notes in Computer Science |
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10882 |
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353-362 |
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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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gtsi @ user @ |
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82 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud |
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Title |
Near InfraRed Imagery Colorization |
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Conference Article |
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2018 |
Publication |
25 th IEEE International Conference on Image Processing, ICIP 2018 |
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2237-2241 |
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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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gtsi @ user @ |
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81 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Adaptive Harris Corners Detector Evaluated with Cross-Spectral Images |
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Conference Article |
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Year |
2018 |
Publication |
International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing |
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721 |
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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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gtsi @ user @ |
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84 |
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Author |
Wilton Agila; Gomer Rubio; L. Miranda; L. Vázquez |
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Title |
Qualitative Model of Control in the Pressure Stabilization of PEM Fuel Cell |
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Conference Article |
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2018 |
Publication |
7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. |
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1221-1226 |
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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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gtsi @ user @ |
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88 |
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Author |
Gomer Rubio; Wilton Agila |
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Title |
Dynamic Modeling of Fuel Cells in a Strategic Context |
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Conference Article |
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2018 |
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7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia. |
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gtsi @ user @ |
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86 |
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Author |
Del Pino, J.; Salazar, G.; Cedeño, V. Msc. |
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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 |
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Journal Article |
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2011 |
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Revista Tecnológica ESPOL |
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Vol. 24 |
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pp. 29 - 34 |
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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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Campus “Gustavo Galindo Velasco” La prosperina Km 30,5 vía perimetral, Guayaquil, Ecuador |
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Español |
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Español |
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cidis @ cidis @ |
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13 |
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Author |
Carlos Monsalve; Alain April; Alain Abran |
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Title |
BPM and requirements elicitation at multiple levels of abstraction: A review |
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2011 |
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IADIS International Conference on Information Systems 2011 |
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237-242 |
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Business process modeling, levels of abstraction, requirements elicitation, requirements modeling, review |
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Business process models can be useful for requirements elicitation, among other things. Software development depends on the quality of the requirements elicitation activities, and so adequately modeling business processes (BPs) is critical. A key factor in achieving this is the active participation of all the stakeholders in the development of a shared vision of BPs.
Unfortunately, organizations often find themselves left with inconsistent BPs that do not cover all the stakeholders’ needs
and constraints. However, consolidation of the various stakeholder requirements may be facilitated through the use of multiple levels of abstraction (MLA). This article contributes to the research into MLA use in business process modeling (BPM) for software requirements by reviewing the theoretical foundations of MLA and their use in various BP-oriented approaches. |
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CIDIS-FIEC, Escuela Superior Politécnica del Litoral (ESPOL) Km. 30.5 vía Perimetral, |
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cidis @ cidis @ |
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15 |
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Mónica Villavicencio; Alain Abran |
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Title |
Facts and Perceptions Regarding Software Measurement in Education and in Practice: Preliminary Results |
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2011 |
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Journal of Software Engineering and Application |
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pp. 227-234 |
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Software measurement, education, software engineering |
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How is software measurement addressed in undergraduate and graduate programs in universities? Do organizations consider that the graduating students they hire have an adequate knowledge of software measurement? To answer these and related questions, a survey was administered to participants who attended the IWSM-MENSURA 2010 conference in Stuttgart, Germany. Forty-seven of the 69 conference participants (including software development practitioners, software measurement consultants, university professors, and graduate students) took part in the survey. The results indicate that software measurement topics are: A) covered mostly at the graduate level and not at the undergraduate level, and B) not mandatory. Graduate students and professors consider that, of the measurement topics covered in university curricula, specific topics, such as measures for the requirements phase, and measurement techniques and tools, receive more attention in the academic context. A common observation of the practitioners who participated in the survey was that students hired as new employees bring limited software measurement-related knowledge to their organizations. Discussion of the findings and directions for future research are presented. |
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2 CIDIS-FIEC, Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil, Ecuador |
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cidis @ cidis @ |
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