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Author |
Marta Diaz; Dennys Paillacho; Cecilio Angulo |
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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. |
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2015 |
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International Journal of Humanoid Robotics |
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12 |
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Group-robot interaction; robotic-guide; social navigation; space management; spatial formations; group walking behavior; crowd behavior |
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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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International Journal of Humanoid Robotics |
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cidis @ cidis @ |
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34 |
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Author |
Marjorie Chalen; Boris X. Vintimilla |
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Title |
Towards Action Prediction Applying Deep Learning |
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2019 |
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Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 |
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action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm. |
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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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cidis @ cidis @ |
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129 |
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Santos V.; Angel D. Sappa.; Oliveira M. & de la Escalera A. |
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Special Issue on Autonomous Driving and Driver Assistance Systems |
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Journal Article |
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2019 |
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In Robotics and Autonomous Systems |
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Vol. 121 |
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gtsi @ user @ |
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119 |
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Author |
Morocho-Cayamcela, M.E. |
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Title |
Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling |
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2020 |
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Electronic Letters on Computer Vision and Image Analysis (ELCVIA) |
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no |
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cidis @ cidis @ |
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140 |
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Author |
Charco, J.L., Sappa, A.D., Vintimilla, B.X., Velesaca, H.O. |
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Title |
Camera pose estimation in multi-view environments:from virtual scenarios to the real world |
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Journal Article |
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2021 |
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In Image and Vision Computing Journal. (Vol. 110. Article number 104182) |
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Relative camera pose estimation, Domain adaptation, Siamese architecture, Synthetic data, Multi-view environments |
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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. |
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English |
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no |
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cidis @ cidis @ |
Serial |
147 |
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Author |
Rubio, G.A., Agila, W.E |
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Title |
A fuzzy model to manage water in polymer electrolyte membrane fuel cells |
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Journal Article |
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2021 |
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In Processes Journal. (Vol. 9, Issue 6, Article number 904) |
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PEM fuel cell, fuzzy, neural network, electrical response, flooding, drying. |
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In this paper, a fuzzy model is presented to determine in real-time the degree of dehydration or flooding of a proton exchange membrane of a fuel cell, to optimize its electrical response and consequently, its autonomous operation. By applying load, current and flux variations in the dry, normal, and flooded states of the membrane, it was determined that the temporal evolution of the fuel cell voltage is characterized by changes in slope and by its voltage oscillations. The results were validated using electrochemical impedance spectroscopy and show slope changes from 0.435 to 0.52 and oscillations from 3.6 mV to 5.2 mV in the dry state, and slope changes from 0.2 to 0.3 and oscillations from 1 mV to 2 mV in the flooded state. The use of fuzzy logic is a novelty and constitutes a step towards the progressive automation of the supervision, perception, and intelligent control of fuel cells, allowing them to reduce their risks and increase their economic benefits. |
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no |
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cidis @ cidis @ |
Serial |
153 |
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Author |
Michael Teutsch, Angel Sappa & Riad Hammoud |
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Title |
Computer Vision in the Infrared Spectrum: Challenges and ApproachesComputer Vision in the Infrared Spectrum: Challenges and Approaches |
Type |
Journal Article |
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2021 |
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Synthesis Lectures on Computer Vision. Volumen 10, No. 2 Pages 138 |
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cidis @ cidis @ |
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166 |
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Author |
Santos, V., Sappa, A.D., Oliveira, M. & de la Escalera, A. |
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Editorial: Special Issue on Autonomous Driving and Driver Assistance Systems – Some Main Trends |
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Journal Article |
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2021 |
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In Journal: Robotics and Autonomous Systems. (Vol. 144, Article number 103832) |
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cidis @ cidis @ |
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158 |
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Author |
Velesaca, H.O., Suárez, P. L., Mira, R., & Sappa, A.D. |
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Title |
Computer Vision based Food Grain Classification: a Comprehensive Survey |
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2021 |
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In Computers and Electronics in Agriculture Journal. (Vol. 187, Article number 106287) |
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cidis @ cidis @ |
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159 |
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Pereira J., Mora M. & W. Agila |
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Title |
Qualitative Model to Maximize Shrimp Growth at Low Cost |
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2021 |
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5th Ecuador Technical Chapters Meeting (ETCM 2021), Octubre 12 – 15 |
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cidis @ cidis @ |
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167 |
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