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Patricia Súarez, H. V., Dario Carpio & Angel Sappa. (2023). Corn Kernel Classification From Few Training Samples. In journal Artificial Intelligence in Agriculture, Vol. 9, pp. 89–99.
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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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G.A. Rubio, & Wilton Agila. (2019). Sustainable Energy: A Strategic View of Fuel Cells. In 8th International Conference on Renewable Energy Research and Applications (ICRERA 2019); Brasov, Rumania (pp. 239–243).
Abstract: Based on the model of the proton exchange fuel cell in a strategic context,
this document develops the issue of energy as one of the pillars to achieve the
sustainability of our planet, considering the future scenarios up to the year 2060 of the
situation energy, hydrogen as a strategic vector and the contribution of the fuel cell in
solving the serious problems of environmental pollution and economic inequity that
humanity faces; for its application in the energy generation, telecommunications and
vehicle manufacturing industries.
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Carlos Monsalve, Alain April, & Alain Abran. (2011). Requirements Elicitation Using BPM Notations: Focusing on the Strategic Level Representation. In 10th WSEAS international conference on Applied computer and applied computational science (pp. 235–241). 1100 rue Notre-Dame Ouest, Montréal, Québec H3C 1K3 CANADA.
Abstract: Business process models (BPM) can be useful for requirements elicitation, among other uses. Since the active participation of all stakeholders is a key factor for successful requirements engineering, it is important that BPM be shared by all stakeholders. Unfortunately, organizations may end up with inconsistent BPM not covering all stakeholders’ needs and constraints. The use of multiple levels of abstraction (MLA), such as at the strategic, tactical and operational levels, is often used in various process-oriented initiatives to facilitate the consolidation of various stakeholders’ needs and constraints. This article surveys the use of MLA in recent BPM research publications and reports on a BPM action-research case study conducted in a Canadian organization, with the aim of exploring the usefulness of the strategic level.
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Carlos Monsalve, Alain April, & Alain Abran. (2011). BPM and requirements elicitation at multiple levels of abstraction: A review. In IADIS International Conference on Information Systems 2011 (pp. 237–242).
Abstract: Business process models can be useful for requirements elicitation, among other things. Software development depends on the quality of the requirements elicitation activities, and so adequately modeling business processes (BPs) is critical. A key factor in achieving this is the active participation of all the stakeholders in the development of a shared vision of BPs.
Unfortunately, organizations often find themselves left with inconsistent BPs that do not cover all the stakeholders’ needs
and constraints. However, consolidation of the various stakeholder requirements may be facilitated through the use of multiple levels of abstraction (MLA). This article contributes to the research into MLA use in business process modeling (BPM) for software requirements by reviewing the theoretical foundations of MLA and their use in various BP-oriented approaches.
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Patricia L. Suarez, Angel D. Sappa, & Boris X. Vintimilla. (2019). Image patch similarity through a meta-learning metric based approach. In 15th International Conference on Signal Image Technology & Internet based Systems (SITIS 2019); Sorrento, Italia (pp. 511–517).
Abstract: Comparing images regions are one of the core methods used on computer vision for tasks like image classification, scene understanding, object detection and recognition. Hence, this paper proposes a novel approach to determine similarity of image regions (patches), in order to obtain the best representation of image patches. This problem has been studied by many researchers presenting different approaches, however, the ability to find the better criteria to measure the similarity on image regions are still a challenge. The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results
have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results.
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Roberto Jacome Galarza, Miguel-Andrés Realpe-Robalino, Chamba-Eras LuisAntonio, & Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco. (2019). Computer vision for image understanding. A comprehensive review. In International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador (pp. 248–259).
Abstract: Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction.
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Ma. Paz Velarde, Erika Perugachi, Dennis G. Romero, Ángel D. Sappa, & Boris X. Vintimilla. (2015). Análisis del movimiento de las extremidades superiores aplicado a la rehabilitación física de una persona usando técnicas de visión artificial. Revista Tecnológica ESPOL-RTE, Vol. 28, pp. 1–7.
Abstract: Comúnmente durante la rehabilitación física, el diagnóstico dado por el especialista se basa en observaciones cualitativas que sugieren, en algunos casos, conclusiones subjetivas. El presente trabajo propone un enfoque cuantitativo, orientado a servir de ayuda a fisioterapeutas, a través de una herramienta interactiva y de bajo costo que permite medir los movimientos de miembros superiores. Estos movimientos son capturados por un sensor RGB-D y procesados mediante la metodología propuesta, dando como resultado una eficiente representación de movimientos, permitiendo la evaluación cuantitativa de movimientos de los miembros superiores.
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Marjorie Chalen, & Boris X. Vintimilla. (2019). Towards Action Prediction Applying Deep Learning. Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019, , pp. 1–3.
Abstract: 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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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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