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	Author	Henry O. Velesaca; Raul A. Mira; Patricia L. Suarez; Christian X. Larrea; Angel D. Sappa.
	Title	Deep Learning based Corn Kernel Classification.			Type	Conference Article
	Year	2020	Publication	The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture on the Conference Computer on Vision and Pattern Recongnition (CVPR 2020)	Abbreviated Journal
	Volume	2020-June	Issue	9150684	Pages	294-302
	Keywords
	Abstract	This paper presents a full pipeline to classify sample sets of corn kernels. The proposed approach follows a segmentation-classification scheme. The image segmentation is performed through a well known deep learning based approach, the Mask R-CNN architecture, while the classification is performed by means of a novel-lightweight network specially designed for this task—good corn kernel, defective corn kernel and impurity categories are considered. As a second contribution, a carefully annotated multitouching corn kernel dataset has been generated. This dataset has been used for training the segmentation and the classification modules. Quantitative evaluations have been performed and comparisons with other approaches provided showing improvements with the proposed pipeline.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	21607508	ISBN	978-172819360-1	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	cidis @ cidis @			Serial	124
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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	Nayeth I. Solorzano Alcivar, Robert Loor, Stalyn Gonzabay Yagual, & Boris X. Vintimilla
	Title	Statistical Representations of a Dashboard to Monitor Educational Videogames in Natural Language			Type	Conference Article
	Year	2020	Publication	ETLTC – ACM Chapter: International Conference on Educational Technology, Language and Technical Communication; Fukushima, Japan, 27-31 Enero 2020	Abbreviated Journal
	Volume	77	Issue		Pages
	Keywords
	Abstract	This paper explains how Natural Language (NL) processing by computers, through smart programs as a way of Machine Learning (ML), can represent large sets of quantitative data as written statements. The study recognized the need to improve the implemented web platform using a dashboard in which we collected a set of extensive data to measure assessment factors of using children´s educational games. In this case, applying NL is a strategy to give assessments, build, and display more precise written statements to enhance the understanding of children´s gaming behavior. We propose the development of a new tool to assess the use of written explanations rather than a statistical representation of feedback information for the comprehension of parents and teachers with a lack of primary level knowledge in statistics. Applying fuzzy logic theory, we present verbatim explanations of children´s behavior playing educational videogames as NL interpretation instead of statistical representations. An educational series of digital game applications for mobile devices, identified as MIDI (Spanish acronym of “Interactive Didactic Multimedia for Children”) linked to a dashboard in the cloud, is evaluated using the dashboard metrics. MIDI games tested in local primary schools helps to evaluate the results of using the proposed tool. The guiding results allow analyzing the degrees of playability and usability factors obtained from the data produced when children play a MIDI game. The results obtained are presented in a comprehensive guiding evaluation report applying NL for parents and teachers. These guiding evaluations are useful to enhance children's learning understanding related to the school curricula applied to ludic digital games.
	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	131
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	Author	Angel D. Sappa; Juan A. Carvajal; Cristhian A. Aguilera; Miguel Oliveira; Dennis G. Romero; Boris X. Vintimilla
	Title	Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study			Type	Journal Article
	Year	2016	Publication	Sensors Journal	Abbreviated Journal
	Volume	Vol. 16	Issue		Pages	pp. 1-15
	Keywords	image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform
	Abstract	This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and LongWave InfraRed (LWIR).
	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	47
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	Author	Low S., Inkawhich N., Nina O., Sappa A. and Blasch E.
	Title	Multi-modal Aerial View Object Classification Challenge Results-PBVS 2022.			Type	Conference Article
	Year	2022	Publication	Conference on Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24.	Abbreviated Journal	CONFERENCE
	Volume	2022-June	Issue		Pages	417-425
	Keywords
	Abstract	This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. This year’s MAVOC challenge is the second iteration. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) input modalities. Teams are encouraged/challenged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal models. Using the same UNIfied COincident Optical and Radar for recognitioN (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two techniques, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform our baseline. For SAR classification, the top team showed a 129% improvement over our baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021.
	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	177
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	Author	Marta Diaz; Dennys Paillacho; Cecilio Angulo
	Title	Evaluating Group-Robot Interaction in Crowded Public Spaces: A Week-Long Exploratory Study in the Wild with a Humanoid Robot Guiding Visitors Through a Science Museum.			Type	Journal Article
	Year	2015	Publication	International Journal of Humanoid Robotics	Abbreviated Journal
	Volume	Vol. 12	Issue		Pages
	Keywords	Group-robot interaction; robotic-guide; social navigation; space management; spatial formations; group walking behavior; crowd behavior
	Abstract	This paper describes an exploratory study on group interaction with a robot-guide in an open large-scale busy environment. For an entire week a humanoid robot was deployed in the popular Cosmocaixa Science Museum in Barcelona and guided hundreds of people through the museum facilities. The main goal of this experience is to study in the wild the episodes of the robot guiding visitors to a requested destination focusing on the group behavior during displacement. The walking behavior follow-me and the face to face communication in a populated environment are analyzed in terms of guide- visitors interaction, grouping patterns and spatial formations. Results from observational data show that the space configurations spontaneously formed by the robot guide and visitors walking together did not always meet the robot communicative and navigational requirements for successful guidance. Therefore additional verbal and nonverbal prompts must be considered to regulate effectively the walking together and follow-me behaviors. Finally, we discuss lessons learned and recommendations for robot’s spatial behavior in dense crowded scenarios.
	Address
	Corporate Author				Thesis
	Publisher	International Journal of Humanoid Robotics	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	34
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	Author	Dennis G. Romero; A. F. Neto; T. F. Bastos; Boris X. Vintimilla
	Title	An approach to automatic assistance in physiotherapy based on on-line movement identification.			Type	Conference Article
	Year	2012	Publication	VI Andean Region International Conference – ANDESCON 2012	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	patient rehabilitation, patient treatment, statistical analysis
	Abstract	This paper describes a method for on-line movement identification, oriented to patient’s movement evaluation during physiotherapy. An analysis based on Mahalanobis distance between temporal windows is performed to identify the “idle/motion” state, which defines the beginning and end of the patient’s movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments.
	Address
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication	Andean Region International Conference (ANDESCON), 2012 VI	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	24
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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	Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa
	Title	Feature Point Descriptors: Infrared and Visible Spectra			Type	Journal Article
	Year	2014	Publication	Sensors Journal	Abbreviated Journal
	Volume	Vol. 14	Issue		Pages	pp. 3690-3701
	Keywords	cross-spectral imaging; feature point descriptors
	Abstract	This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.
	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	28
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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
Permanent link to this record