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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. (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	Luis Chuquimarca, Boris X. Vintimilla & Sergio Velastin
	Title	Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models			Type	Conference Article
	Year	2024	Publication	14th International Conference on Pattern Recognition Systems (ICPRS) Londres 15 – 18 July 2024	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	979-835037565-7	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	245
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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	Armin Mehri; Angel D. Sappa
	Title	Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples			Type	Conference Article
	Year	2019	Publication	Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States	Abbreviated Journal
	Volume		Issue		Pages	971-979
	Keywords
	Abstract	This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation metrics—and qualitatively evaluated showing considerable improvements with respect to the state of the art
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	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	105
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	Author	Velesaca, H.O., Suárez, P. L., Mira, R., & Sappa, A.D.
	Title	Computer Vision based Food Grain Classification: a Comprehensive Survey			Type	Journal Article
	Year	2021	Publication	In Computers and Electronics in Agriculture Journal. (Article number 106287)	Abbreviated Journal
	Volume	Vol. 187	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	159
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	Author	Roberto Jacome Galarza; Miguel-Andrés Realpe-Robalino; Chamba-Eras LuisAntonio; Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco
	Title	Computer vision for image understanding. A comprehensive review			Type	Conference Article
	Year	2019	Publication	International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador	Abbreviated Journal
	Volume		Issue		Pages	248-259
	Keywords
	Abstract	Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction.
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	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	97
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	Author	Michael Teutsch, Angel Sappa & Riad Hammoud
	Title	Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches			Type	Journal Article
	Year	2021	Publication	Synthesis Lectures on Computer Vision	Abbreviated Journal
	Volume	Vol. 10 No. 2	Issue		Pages	pp. 138
	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	166
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	Author	M. Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel D. Sappa; A. Tomé
	Title	Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains			Type	Conference Article
	Year	2015	Publication	Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015	Abbreviated Journal
	Volume		Issue		Pages	2488 - 2495
	Keywords	Birds, Training, Legged locomotion, Visualization, Histograms, Object recognition, Gaussian mixture model
	Abstract	In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline.
	Address
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication	Hamburg, Germany	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/RSJ International Conference on Intelligent Robots and Systems (IROS)
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	41
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	Author	Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa
	Title	Corn Kernel Classification From Few Training Samples			Type	Journal Article
	Year	2023	Publication	In journal Artificial Intelligence in Agriculture	Abbreviated Journal
	Volume	Vol. 9	Issue		Pages	pp. 89-99
	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	25897217	ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	223
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	Author	Jacome-Galarza L.-R
	Title	Crop yield prediction utilizing multimodal deep learning			Type	Conference Article
	Year	2021	Publication	16th Iberian Conference on Information Systems and Technologies, CISTI 2021, junio 23 – 26, 2021	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Agricultura de precisión; sensores remotos; aprendizaje profundo multimodal; IoT; agentes inteligentes; computación aplicada.
	Abstract	La agricultura de precisión es una práctica vital para mejorar la producción de cosechas. El presente trabajo tiene como objetivo desarrollar un modelo multimodal de aprendizaje profundo que es capaz de producir un mapa de salud de cosechas. El modelo recibe como entradas imágenes multiespectrales y datos de sensores de campo (humedad, temperatura, estado del suelo, etc.) y crea un mapa de rendimiento de la cosecha. La utilización de datos multimodales tiene como finalidad extraer patrones ocultos del estado de salud de las cosechas y de esta manera obtener mejores resultados que los obtenidos mediante los índices de vegetación.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	Español	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	150
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