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	Author	Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo
	Title	Learning crossspectral similarity measures with deep convolutional neural networks			Type	Conference Article
	Year	2016	Publication	IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops	Abbreviated Journal
	Volume		Issue		Pages	267-275
	Keywords
	Abstract	The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	48
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	Author	Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira
	Title	Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives			Type	Journal Article
	Year	2016	Publication	Robotics and Autonomous Systems Journal	Abbreviated Journal
	Volume	Vol. 83	Issue		Pages	pp. 312-325
	Keywords	Incremental scene reconstructionPoint cloudsAutonomous vehiclesPolygonal primitives
	Abstract	When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	49
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	Author	Monica Villavicencio; Alain Abran
	Title	Educational Issues in the Teaching of Software Measurement in Software Engineering Undergraduate Programs			Type	Conference Article
	Year	2011	Publication	Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement	Abbreviated Journal
	Volume		Issue		Pages	239-244
	Keywords	measurement; software engineering; higher education
	Abstract	In mature engineering disciplines and science, mathematics and measurement are considered as important subjects to be taught in university programs. This paper discusses about these subjects in terms of their respective meanings and complementarities. It also presents a discussion regarding their maturity, relevance and innovations in their teaching in engineering programs. This paper pays special attention to the teaching of software measurement in higher education, in particular with respect to mathematics and measurement in engineering in general. The findings from this analysis will be useful for researchers and educators interested in the enhancement of educational issues related to software measurement.
	Address
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	gtsi @ user @			Serial	68
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	Author	Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira
	Title	Incremental Texture Mapping for Autonomous Driving			Type	Journal Article
	Year	2016	Publication	Robotics and Autonomous Systems Journal	Abbreviated Journal
	Volume	Vol. 84	Issue		Pages	pp. 113-128
	Keywords	Scene reconstruction, Autonomous driving, Texture mapping
	Abstract	Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	50
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	Author	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.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	51
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	Author	Angel D. Sappa; Cristhian A. Aguilera; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo
	Title	Monocular visual odometry: a cross-spectral image fusion based approach			Type	Journal Article
	Year	2016	Publication	Robotics and Autonomous Systems Journal	Abbreviated Journal
	Volume	Vol. 86	Issue		Pages	pp. 26-36
	Keywords	Monocular visual odometry LWIR-RGB cross-spectral imaging Image fusion
	Abstract	This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is em- pirically obtained by means of a mutual information based evaluation met- ric. The objective is to have a exible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odom- etry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	Enlgish	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	54
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	Author	Cristhian A. Aguilera, Cristhian Aguilera, Cristóbal A. Navarro, & Angel D. Sappa
	Title	Fast CNN Stereo Depth Estimation through Embedded GPU Devices			Type	Journal Article
	Year	2020	Publication	Sensors 2020	Abbreviated Journal
	Volume	Vol. 2020-June	Issue	11	Pages	pp. 1-13
	Keywords	stereo matching; deep learning; embedded GPU
	Abstract	Current CNN-based stereo depth estimation models can barely run under real-time constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art evaluations usually do not consider model optimization techniques, being that it is unknown what is the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models on three different embedded GPU devices, with and without optimization methods, presenting performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically augmenting the runtime speed of current models. In our experiments, we achieve real-time inference speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier, and Jetson Nano embedded devices.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	14248220	ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	132
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	Author	Ángel Morera, Ángel Sánchez, A. Belén Moreno, Angel D. Sappa, & José F. Vélez
	Title	SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities.			Type	Journal Article
	Year	2020	Publication		Abbreviated Journal	In Sensors
	Volume	Vol. 2020-August	Issue	16	Pages	pp. 1-23
	Keywords	object detection; urban outdoor panels; one-stage detectors; Single Shot MultiBox Detector (SSD); You Only Look Once (YOLO); detection metrics; object and scene imaging variabilities
	Abstract	This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) deep neural networks for the outdoor advertisement panel detection problem by handling multiple and combined variabilities in the scenes. Publicity panel detection in images oers important advantages both in the real world as well as in the virtual one. For example, applications like Google Street View can be used for Internet publicity and when detecting these ads panels in images, it could be possible to replace the publicity appearing inside the panels by another from a funding company. In our experiments, both SSD and YOLO detectors have produced acceptable results under variable sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex background and multiple panels in scenes. Due to the diculty of finding annotated images for the considered problem, we created our own dataset for conducting the experiments. The major strength of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable when the publicity contained inside the panel is analyzed after detecting them. On the other side, YOLO produced better panel localization results detecting a higher number of True Positive (TP) panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models with dierent types of semantic segmentation networks and using the same evaluation metrics is also included.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	14248220	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	133
Permanent link to this record



	Author	Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O.
	Title	Camera pose estimation in multi-view environments:from virtual scenarios to the real world			Type	Journal Article
	Year	2021	Publication	In Image and Vision Computing Journal. (Article number 104182)	Abbreviated Journal
	Volume	Vol. 110	Issue		Pages
	Keywords	Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments
	Abstract	This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	147
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	Author	Del Pino, J.; Salazar, G.; Cedeño, V. Msc.
	Title	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			Type	Journal Article
	Year	2011	Publication	Revista Tecnológica ESPOL	Abbreviated Journal
	Volume	Vol. 24	Issue		Pages	pp. 29 - 34
	Keywords
	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
	Address	Campus “Gustavo Galindo Velasco” La prosperina Km 30,5 vía perimetral, Guayaquil, Ecuador
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	Español	Summary Language	Español	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	13
Permanent link to this record