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Henry O. Velesaca, D. C., Angel D. Sappa, Juan A. Holgado-Terriza & Wilton Agila. (2026). Industrial Anomaly Detection: Bridging OPC-UA with Classical and Deep Learning Methods. In Communications in Computer and Information Science. 4th International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2024, Quito 2-4 Diciembre 2024 (Vol. 2392 CCIS, pp. 3–18).
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Henry O. Velesaca, M. R., Angel D. Sappa & Alice Gomez. (2026). Analysis of Hidden Patterns in Road Accident Dataset using Clustering Techniques. In Communications in Computer and Information Science. 4th International Conference on Smart Technologies, Systems and Applications, SmartTech-IC 2024, Quito 2-4 Diciembre 2024 (Vol. 2393 CCIS, 96–111).
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Calderón, E., Riofrio, A., Baykara, H., Realpe, M., & Paillacho, J. (2025). Predictive Modeling of Ecuadorian Zeolite-Based Geopolymer Compressive Strength: A Machine Learning Approach. In Communications in Computer and Information Science. 6th International Conference on International Conference on Applied Technologies, ICAT 2024 Samborondon, 20-22 noviembre 2024 (Vol. Vol. 2457 CCIS, pp. 14–29).
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Ricardo Cajo, & Wilton Agila. (2015). Evaluation of algorithms for linear and nonlinear PID control for Twin Rotor MIMO System. In Computer Aided System Engineering (APCASE), 2015 Asia-Pacific Conference on, Quito, 2015 (pp. 214–219). IEEE.
Abstract: In this paper the linear and nonlinear PID control algorithms are analyzed and for a twin rotor MIMO system (TRMS), whose characteristic is not linear with two degrees of freedom and cross-links. The aim of this work is to stabilize the TRMS, to achieve a particular position and follow a trajectory in the shortest time. Mathematical modeling of helicopter model is simulated using MATLAB / Simulink, the two degrees of freedom are controlled both horizontally and vertically through the proposed controllers. Also nonlinear segmented observers for each degree of freedom are designed in order to measure statements required by the nonlinear controller. Followed, a comparative analysis of both algorithms is presented to evaluate their performance in the real TRMS.
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Rafael E. Rivadeneira, A. D. S., Boris X. Vintimilla, Jin Kim, Dogun Kim et al. (2022). Thermal Image Super-Resolution Challenge Results- PBVS 2022. In Computer Vision and Pattern Recognition Workshops, (CVPRW 2022), junio 19-24. (Vol. 2022-June, pp. 349–357).
Abstract: This paper presents results from the third Thermal Image
Super-Resolution (TISR) challenge organized in the Perception Beyond the Visible Spectrum (PBVS) 2022 workshop.
The challenge uses the same thermal image dataset as the
first two challenges, with 951 training images and 50 validation images at each resolution. A set of 20 images was
kept aside for testing. The evaluation tasks were to measure
the PSNR and SSIM between the SR image and the ground
truth (HR thermal noisy image downsampled by four), and
also to measure the PSNR and SSIM between the SR image
and the semi-registered HR image (acquired with another
camera). The results outperformed those from last year’s
challenge, improving both evaluation metrics. This year,
almost 100 teams participants registered for the challenge,
showing the community’s interest in this hot topic.
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P. Ricaurte, C. Chilán, C. A. Aguilera-Carrasco, B. X. Vintimilla, & Angel D. Sappa. (2014). Performance Evaluation of Feature Point Descriptors in the Infrared Domain. In Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2013 (Vol. 1, pp. 545–550). IEEE.
Abstract: This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered.
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A. Amato, F. Lumbreras, & Angel D. Sappa. (2014). A general-purpose crowdsourcing platform for mobile devices. In Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 (Vol. 3, pp. 211–215). Lisbon, Portugal: IEEE.
Abstract: This paper presents details of a general purpose micro-taskon-demand platform based on the crowdsourcing philosophy. This platformwas specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquityand iii) embedded sensors.The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks.Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and task- solver).Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way.Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications.Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform.
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N. Onkarappa, Cristhian A. Aguilera, B. X. Vintimilla, & Angel D. Sappa. (2014). Cross-spectral Stereo Correspondence using Dense Flow Fields. In Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 (Vol. 3, pp. 613–617). IEEE.
Abstract: This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense flow field based representation instead of the original cross-spectral images, which have a low correlation. In this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary experimental results on urban environments have been obtained showing the validity of the proposed approach.
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Armin Mehri, & Angel D. Sappa. (2019). Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples. In Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States (pp. 971–979).
Abstract: This paper presents a novel approach for colorizing
near infrared (NIR) images. The approach is based on
image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored
networks that require less computation times, converge
faster and generate high quality samples. The obtained results have been quantitatively—using standard evaluation
metrics—and qualitatively evaluated showing considerable
improvements with respect to the state of the art
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Patricia L. Suarez, Angel D. Sappa, Boris X. Vintimilla, & Riad I. Hammoud. (2019). Image Vegetation Index through a Cycle Generative Adversarial Network. In Conference on Computer Vision and Pattern Recognition Workshops (CVPR 2019); Long Beach, California, United States (pp. 1014–1021).
Abstract: This paper proposes a novel approach to estimate the
Normalized Difference Vegetation Index (NDVI) just from
an RGB image. The NDVI values are obtained by using
images from the visible spectral band together with a synthetic near infrared image obtained by a cycled GAN. The
cycled GAN network is able to obtain a NIR image from
a given gray scale image. It is trained by using unpaired
set of gray scale and NIR images by using a U-net architecture and a multiple loss function (gray scale images are
obtained from the provided RGB images). Then, the NIR
image estimated with the proposed cycle generative adversarial network is used to compute the NDVI index. Experimental results are provided showing the validity of the proposed approach. Additionally, comparisons with previous
approaches are also provided.
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