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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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Morocho-Cayamcela, M. E. (2020). Increasing the Segmentation Accuracy of Aerial Images with Dilated Spatial Pyramid Pooling. Electronic Letters on Computer Vision and Image Analysis (ELCVIA), Vol. 19(Issue 2), pp. 17–21.
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Cristhian A. Aguilera, C. A., Cristóbal A. Navarro, & Angel D. Sappa. (2020). Fast CNN Stereo Depth Estimation through Embedded GPU Devices. Sensors 2020, Vol. 2020-June(11), pp. 1–13.
Abstract: Current CNN-based stereo depth estimation models can barely run under real-time
constraints on embedded graphic processing unit (GPU) devices. Moreover, state-of-the-art
evaluations usually do not consider model optimization techniques, being that it is unknown what is
the current potential on embedded GPU devices. In this work, we evaluate two state-of-the-art models
on three different embedded GPU devices, with and without optimization methods, presenting
performance results that illustrate the actual capabilities of embedded GPU devices for stereo depth
estimation. More importantly, based on our evaluation, we propose the use of a U-Net like architecture
for postprocessing the cost-volume, instead of a typical sequence of 3D convolutions, drastically
augmenting the runtime speed of current models. In our experiments, we achieve real-time inference
speed, in the range of 5–32 ms, for 1216 368 input stereo images on the Jetson TX2, Jetson Xavier,
and Jetson Nano embedded devices.
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Rosero Vasquez Shendry. (2020). Facial recognition: traditional methods vs. methods based on deep learning. Advances in Intelligent Systems and Computing – Information Technology and Systems Proceedings of ICITS 2020.615–625.
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Xavier Soria, Edgar Riba, & Angel D. Sappa. (2020). Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection. In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 1912–1921).
Abstract: This paper proposes a Deep Learning based edge de- tector, which is inspired on both HED (Holistically-Nested Edge Detection) and Xception networks. The proposed ap- proach generates thin edge-maps that are plausible for hu- man eyes; it can be used in any edge detection task without previous training or fine tuning process. As a second contri- bution, a large dataset with carefully annotated edges, has been generated. This dataset has been used for training the proposed approach as well the state-of-the-art algorithms for comparisons. Quantitative and qualitative evaluations have been performed on different benchmarks showing im- provements with the proposed method when F-measure of ODS and OIS are considered.
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Henry O. Velesaca, Raul A. Mira, Patricia L. Suarez, Christian X. Larrea, & Angel D. Sappa. (2020). Deep Learning based Corn Kernel Classification. In 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) (Vol. 2020-June, pp. 294–302).
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.
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