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Patricia Suarez, A. S. (2024). Depth-Conditioned Thermal-like Image Generation. In Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS).
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Omar Coello, M. C., Darío Carpio, Boris X. Vintimilla & Luis Chuquimarca. (2024). Enhancing Apple’s Defect Classification: Insights from Visible Spectrum and Narrow Spectral Band Imaging. In Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS).
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Luis Chuquimarca, B. X. V. & S. V. (2024). Classifying Healthy and Defective Fruits with a Siamese Architecture and CNN Models. In Accepted in 14th International Conference on Pattern Recognition Systems (ICPRS).
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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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Ángel Morera, Á. S., A. Belén Moreno, Angel D. Sappa, & José F. Vélez. (2020). SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities. In Sensors, Vol. 2020-August(16), pp. 1–23.
Abstract: This work compares Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO)
deep neural networks for the outdoor advertisement panel detection problem by handling multiple
and combined variabilities in the scenes. Publicity panel detection in images o ers important
advantages both in the real world as well as in the virtual one. For example, applications like Google
Street View can be used for Internet publicity and when detecting these ads panels in images, it could
be possible to replace the publicity appearing inside the panels by another from a funding company.
In our experiments, both SSD and YOLO detectors have produced acceptable results under variable
sizes of panels, illumination conditions, viewing perspectives, partial occlusion of panels, complex
background and multiple panels in scenes. Due to the diculty of finding annotated images for the
considered problem, we created our own dataset for conducting the experiments. The major strength
of the SSD model was the almost elimination of False Positive (FP) cases, situation that is preferable
when the publicity contained inside the panel is analyzed after detecting them. On the other side,
YOLO produced better panel localization results detecting a higher number of True Positive (TP)
panels with a higher accuracy. Finally, a comparison of the two analyzed object detection models
with di erent types of semantic segmentation networks and using the same evaluation metrics is
also included.
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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, 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, S. A., Patricia L. Suarez, Ángel Sanchez & Angel D. Sappa. (2020). Off-the-Shelf Based System for Urban Environment Video Analytics. In The 27th International Conference on Systems, Signals and Image Processing (IWSSIP 2020) (Vol. 2020-July, pp. 459–464).
Abstract: This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary
information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to
evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.
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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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Rafael E. Rivadeneira, Angel D. Sappa, Boris X. Vintimilla, Lin Guo, Jiankun Hou, Armin Mehri, et al. (2020). Thermal Image Super-Resolution Challenge – PBVS 2020. In The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) (Vol. 2020-June, pp. 432–439).
Abstract: This paper summarizes the top contributions to the first challenge on thermal image super-resolution (TISR) which was organized as part of the Perception Beyond the Visible Spectrum (PBVS) 2020 workshop. In this challenge, a novel thermal image dataset is considered together with stateof-the-art approaches evaluated under a common framework.
The dataset used in the challenge consists of 1021 thermal images, obtained from three distinct thermal cameras at different resolutions (low-resolution, mid-resolution, and high-resolution), resulting in a total of 3063 thermal images. From each resolution, 951 images are used for training and 50 for testing while the 20 remaining images are used for two proposed evaluations. The first evaluation consists of downsampling the low-resolution, midresolution, and high-resolution thermal images by x2, x3 and x4 respectively, and comparing their super-resolution
results with the corresponding ground truth images. The second evaluation is comprised of obtaining the x2 superresolution from a given mid-resolution thermal image and comparing it with the corresponding semi-registered highresolution thermal image. Out of 51 registered participants, 6 teams reached the final validation phase.
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