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	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. (Vol. 110. Article number 104182)	Abbreviated Journal
	Volume		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	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	Cristhian A. Aguilera; Angel D. Sappa; R. Toledo
	Title	LGHD: A feature descriptor for matching across non-linear intensity variations			Type	Conference Article
	Year	2015	Publication	IEEE International Conference on, Quebec City, QC, 2015	Abbreviated Journal
	Volume		Issue		Pages	178 - 181
	Keywords	Feature descriptor, multi-modal, multispectral, NIR, LWIR
	Abstract	This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art.
	Address
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication	Quebec City, QC, Canada	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	2015 IEEE International Conference on Image Processing (ICIP)
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	40
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	Author	Cristhian A. Aguilera; Angel D. Sappa; Ricardo Toledo
	Title	Cross-Spectral Local Descriptors via Quadruplet Network			Type	Journal Article
	Year	2017	Publication	In Sensors Journal	Abbreviated Journal
	Volume	17	Issue		Pages	873
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		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	64
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	Author	Cristhian A. Aguilera; Cristhian Aguilera; Angel D. Sappa
	Title	Melamine faced panels defect classification beyond the visible spectrum.			Type	Journal Article
	Year	2018	Publication	In Sensors 2018	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		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	89
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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
	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	Cristhian A. Aguilera; Xaver Soria; Angel D. Sappa; Ricardo Toledo
	Title	RGBN Multispectral Images: a Novel Color Restoration Approach			Type	Conference Article
	Year	2017	Publication	15th International Conference on Practical Applications of Agents and Multi-Agent Systems	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		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	59
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	Author	Cristina L. Abad; Yi Lu; Roy H. Campbell
	Title	DARE: Adaptive Data Replication for Efficient Cluster Scheduling			Type	Conference Article
	Year	2011	Publication	IEEE International Conference on Cluster Computing, 2011	Abbreviated Journal
	Volume		Issue		Pages	159 - 168
	Keywords	MapReduce, replication, scheduling, locality
	Abstract	Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively.
	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	yes
	Call Number	cidis @ cidis @			Serial	21
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	Author	Daniela Rato, Miguel Oliviera, Victor Santos, Manuel Gomes & Angel Sappa
	Title	A Sensor-to-Pattern Calibration Framework for Multi-Modal Industrial Collaborative Cells.			Type	Journal Article
	Year	2022	Publication	Journal of Manufacturing Systems	Abbreviated Journal
	Volume	Vol. 64	Issue		Pages	pp 497 – 507
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	yes
	Call Number	cidis @ cidis @			Serial	184
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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	24	Issue		Pages	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
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