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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	Jorge L. Charco; Angel D. Sappa; Boris X. Vintimilla; Henry O. Velesaca
	Title	Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem			Type	Conference Article
	Year	2020	Publication	The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020	Abbreviated Journal
	Volume	4	Issue		Pages	498-505
	Keywords	Relative Camera Pose Estimation, Siamese Architecture, Synthetic Data, Deep Learning, Multi-View Environments, Extrinsic Camera Parameters.
	Abstract	This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consist of first training the network using pairs of images from the virtual-world scenario considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data – transfer learning – real-world data) is considered to tackle the limitation on the training due to the reduced number of pairs of real-images on most of the public data sets.
	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	978-989758402-2	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	gtsi @ user @			Serial	120
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	Author	Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla
	Title	Thermal Image Super-Resolution: a Novel Architecture and Dataset			Type	Conference Article
	Year	2020	Publication	The 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020); Valletta, Malta; 27-29 Febrero 2020	Abbreviated Journal
	Volume	4	Issue		Pages	111-119
	Keywords	Thermal images, Far Infrared, Dataset, Super-Resolution.
	Abstract	This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in rig trying to minimize the baseline distance to make easier the registration problem. The proposed architecture is based on ResNet6 as a Generator and PatchGAN as Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available.
	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	978-989758402-2	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	gtsi @ user @			Serial	121
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	Author	Rafael E. Rivadeneira, Angel D. Sappa and Boris X. Vintimilla
	Title	Multi-Image Super-Resolution for Thermal Images.			Type	Conference Article
	Year	2022	Publication	Proceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications VISIGRAPP 2022	Abbreviated Journal
	Volume	4	Issue		Pages	635 - 642
	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	181
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	Author	Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
	Title	Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture			Type	Conference Article
	Year	2017	Publication	19th International Conference on Image Analysis and Processing.	Abbreviated Journal
	Volume		Issue		Pages	287-297
	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	66
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	Author	Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
	Title	Learning Image Vegetation Index through a Conditional Generative Adversarial Network			Type	Conference Article
	Year	2017	Publication	2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM)	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	gtsi @ user @			Serial	70
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	Author	Milton Mendieta; F. Panchana; B. Andrade; B. Bayot; C. Vaca; Boris X. Vintimilla; Dennis G. Romero
	Title	Organ identification on shrimp histological images: A comparative study considering CNN and feature engineering.			Type	Conference Article
	Year	2018	Publication	IEEE Ecuador Technical Chapters Meeting ETCM 2018. Cuenca, Ecuador	Abbreviated Journal
	Volume		Issue		Pages	1-6
	Keywords
	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.
	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	87
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	Author	Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud
	Title	Deep Learning based Single Image Dehazing			Type	Conference Article
	Year	2018	Publication	14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	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.
	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	83
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	Author	Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud
	Title	Near InfraRed Imagery Colorization			Type	Conference Article
	Year	2018	Publication	25 th IEEE International Conference on Image Processing, ICIP 2018	Abbreviated Journal
	Volume		Issue		Pages	2237-2241
	Keywords
	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.
	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	81
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	Author	Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla
	Title	Cross-spectral image dehaze through a dense stacked conditional GAN based approach.			Type	Conference Article
	Year	2018	Publication	14th IEEE International Conference on Signal Image Technology & Internet based Systems (SITIS 2018)	Abbreviated Journal
	Volume		Issue		Pages	358-364
	Keywords
	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.
	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	92
Permanent link to this record