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Author (up) Marjorie Chalen; Boris X. Vintimilla
Title Towards Action Prediction Applying Deep Learning Type Journal Article
Year 2019 Publication Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 Abbreviated Journal
Volume Issue Pages pp. 1-3
Keywords action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm.
Abstract Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task.
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Call Number cidis @ cidis @ Serial 129
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Author (up) 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.
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Publisher International Journal of Humanoid Robotics Place of Publication Editor
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Call Number cidis @ cidis @ Serial 34
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Author (up) Mehri, A, Ardakani, P.B., Sappa, A.D.
Title LiNet: A Lightweight Network for Image Super Resolution Type Conference Article
Year 2021 Publication 25th International Conference on Pattern Recognition (ICPR), enero 10-15, 2021 Abbreviated Journal
Volume Issue Pages 7196-7202
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Call Number cidis @ cidis @ Serial 149
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Author (up) Mehri, A, Ardakani, P.B., Sappa, A.D.
Title MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution. Type Conference Article
Year 2021 Publication In IEEE Winter Conference on Applications of Computer Vision WACV 2021, enero 5-9, 2021 Abbreviated Journal
Volume Issue Pages 2703-2712
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Call Number cidis @ cidis @ Serial 148
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Author (up) 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
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Call Number cidis @ cidis @ Serial 166
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Author (up) Miguel A. Murillo, Julio E. Alvia, & Miguel Realpe
Title Beyond visual and radio line of sight UAVs monitoring system through open software in a simulated environment. Type Conference Article
Year 2021 Publication The 2nd International Conference on Applied Technologies (ICAT 2020), diciembre 2-4. Communications in Computer and Information Science Abbreviated Journal
Volume 1388 Issue Pages 629-642
Keywords Drone, Open Source, Internet, Web Application, Web Server, SITL, Line of sight, UAV.
Abstract The problem of loss of line of sight when operating drones has be-come a reality with adverse effects for professional and amateur drone opera-tors, since it brings technical problems such as loss of data collected by the de-vice in one or more instants of time during the flight and even misunderstand-ings of legal nature when the drone flies over prohibited or private places. This paper describes the implementation of a drone monitoring system using the In-ternet as a long-range communication network in order to avoid the problem of loss of communication between the ground station and the device. For this, a simulated environment is used through an appropriate open software tool. The operation of the system is based on a client that makes requests to a server, the latter in turn communicates with several servers, each of which has a drone connected to it. In the proposed system when a drone is ready to start a flight, its server informs the main server of the system, which in turn gives feedback to the client informing it that the device is ready to carry out the flight; this way customers can send a mission to the device and keep track of its progress in real time on the screen of their web application.
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Call Number cidis @ cidis @ Serial 186
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Author (up) 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.
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Call Number cidis @ cidis @ Serial 49
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Author (up) 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.
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Call Number cidis @ cidis @ Serial 50
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Author (up) 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.
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Call Number cidis @ cidis @ Serial 51
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Author (up) Miguel Realpe; Jonathan S. Paillacho Corredores; Joe Saverio & Allan Alarcon
Title Open Source system for identification of corn leaf chlorophyll contents based on multispectral images Type Conference Article
Year 2019 Publication International Conference on Applied Technologies (ICAT 2019); Quito, Ecuador Abbreviated Journal
Volume Issue Pages 572-581
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Abstract It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows obtaining the chlorophyll level of plants using images.

Python programming language and different libraries of that language were used to develop the solution. It was decided to implement an image labeling module, a simple linear regression and a prediction module. The first module was used to create a database that links the values of the images with those of chlorophyll, which was then used to obtain linear regression in order to determine the relationship between these variables. Finally, the linear

regression was used in the prediction system to obtain chlorophyll values from the images. The linear regression was trained with 92 images, obtaining a root-mean-square error of 7.27 SPAD units. While the testing was perform using 10 values getting a maximum error of 15.5%.

It is concluded that the system is appropriate for chlorophyll contents identification of corn leaves in field tests.

However, it can also be adapted for other measurement and crops. The system can be downloaded at github.com/JoeSvr95/NDVI-Checking [1].
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Call Number gtsi @ user @ Serial 116
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