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Miguel Oliveira, Vítor Santos, Angel D. Sappa, Paulo Dias, & A. Paulo Moreira. (2016). Incremental Texture Mapping for Autonomous Driving. Robotics and Autonomous Systems Journal, Vol. 84, pp. 113–128.
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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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Infrared Image Colorization based on a Triplet DCGAN Architecture. In 13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) (Vol. 2017-July, pp. 212–217).
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Jorge Alvarez Tello, Mireya Zapata, & Dennys Paillacho. (2019). Kinematic optimization of a robot head movements for the evaluation of human-robot interaction in social robotics. In 10th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences (AHFE 2019), Washington D.C.; United States. Advances in Intelligent Systems and Computing (Vol. 975, pp. 108–118).
Abstract: This paper presents the simplification of the head movements from
the analysis of the biomechanical parameters of the head and neck at the
mechanical and structural level through CAD modeling and construction with
additive printing in ABS/PLA to implement non-verbal communication strategies and establish behavior patterns in the social interaction. This is using in the
denominated MASHI (Multipurpose Assistant robot for Social Human-robot
Interaction) experimental robotic telepresence platform, implemented by a
display with a fish-eye camera along with the mechanical mechanism, which
permits 4 degrees of freedom (DoF). In the development of mathematicalmechanical modeling for the kinematics codification that governs the robot and
the autonomy of movement, we have the Pitch, Roll, and Yaw movements, and
the combination of all of them to establish an active communication through
telepresence. For the computational implementation, it will be show the rotational matrix to describe the movement.
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Morocho-Cayamcela, M. E. & W. L. (2020). Lateral confinement of high-impedance surface-waves through reinforcement learning. Electronics Letters, Vol. 56(23, 12 November 2020), pp. 1262–1264.
Abstract: The authors present a model-free policy-based reinforcement learning
model that introduces perturbations on the pattern of a metasurface.
The objective is to learn a policy that changes the size of the
patches, and therefore the impedance in the sides of an artificially structured
material. The proposed iterative model assigns the highest reward
when the patch sizes allow the transmission along a constrained path
and penalties when the patch sizes make the surface wave radiate to
the sides of the metamaterial. After convergence, the proposed
model learns an optimal patch pattern that achieves lateral confinement
along the metasurface. Simulation results show that the proposed
learned-pattern can effectively guide the electromagnetic wave
through a metasurface, maintaining its instantaneous eigenstate when
the homogeneity is perturbed. Moreover, the pattern learned to
prevent reflections by changing the patch sizes adiabatically. The
reflection coefficient S1, 2 shows that most of the power gets transferred
from the source to the destination with the proposed design.
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Xavier Soria, G. P. - J. & A. S. (2022). LDC: Lightweight Dense CNN for Edge Detection. IEEE Access journal, Vol. 10, pp. 68281–68290.
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Cristhian A. Aguilera, Francisco J. Aguilera, Angel D. Sappa, & Ricardo Toledo. (2016). Learning crossspectral similarity measures with deep convolutional neural networks. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (pp. 267–275).
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.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Learning Image Vegetation Index through a Conditional Generative Adversarial Network. In 2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM).
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2017). Learning to Colorize Infrared Images. In 15th International Conference on Practical Applications of Agents and Multi-Agent Systems.
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Constantine Macías A., T. P. A., Realpe Miguel, Suárez Moncada Jenifer, Páez Rosas Diego & Jarrín Enrique Peláez. (2024). Leveraging Deep Learning Techniques for Marine and Coastal Wildlife Using Instance Segmentation: A Study on Galápagos Sea Lions. In 8th Ecuador Technical Chapters Meeting (ETCM 2024) Cuenca, October 15 – October 18, 2024, .
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Leo Thomas Ramos & Angel D. Sappa. (2025). Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery. Scientific Reports, .
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