Examinando por Autor "Cabido, Raúl"

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Computer vision systems and methods for modeling three dimensional structures using two-dimensional segments detected in digital aerial images
(2022-11-08) Esteban, José Luis; Cabido, Raúl; Rivas, Francisco
A system for modeling a roof of a structure comprising a first database, a second database and a processor in communication with the first database and the second database. The processor selects one or more images and the respective metadata thereof from the first database based on a received a geospatial region of interest. The processor generates two-dimensional line segment geometries in pixel space based on two-dimensional outputs generated by a neural network in pixel space of at least one roof structure present in the selected one or more images. The processor classifies the generated two-dimensional line segment geometries into at least one contour graph based on three-dimensional data received from the second database and generates a three-dimensional representation of the at least one roof structure based on the at least one contour graph and the received three-dimensional data.
Multiview 3D human pose estimation using improved least-squares and LSTM networks
(Elsevier, 2019-01-05) Núñez, Juan Carlos; Cabido, Raúl; Vélez, José F.; S. Montemayor, Antonio; Pantrigo, Juan José
In this paper we present a deep learning based method to estimate the human pose in 3D when multiple 2D views are available. Our system is composed of a cascade of specialized systems. Firstly, 2D poses are obtained using a deep neural network for the detection of skeleton keypoints in each available view. Then, the 3D coordinates of each keypoint are reconstructed with our proposed least squares optimization method, that analyzes the quality of the 2D detections to decide either to consider or reject them. Once the 3D poses are obtained for each time step, full body pose estimation is performed with a long short-term memory (LSTM) neural network, that takes advantage of the process history to refine the final pose estimation. We provide evidence of the suitability of our contributions in an extensive experimental study. Finally, we were able to prove experimentally that our method obtains competitive results when it is compared to recent representative works in the literature.
NATURAL LANGUAGE PROCESSING APPLIED FOR TEACHING EVALUATION
(ICERI2022 Proceedings, 2022-11-09) Montalvo, Soto; Rodríguez, Miguel Ángel; Cabido, Raúl; Concha, David
In the context of higher education, the improvement of teaching quality is a constant challenge. Student ratings of teaching are often considered fundamental for measuring the quality of teaching, educational development, and the enhancement of student learning. In the university context, the institution develops methods for measuring teaching quality and teacher effectiveness defining the process of collecting data and making judgments. However, for decades, there has been a debate about whether student ratings of teaching be trusted. Different studies have shown that several factors influence student ratings as teacher body language, timing, or student fatigue, among others. On the other hand, student ratings and student learning are unrelated. The surveys must become the starting point for critical discourse on effective teaching practices. Teaching thousands of students without engaging them in discussions about their learning experiences is not rational. In this sense, we propose an easy way to collect the students' opinions for teaching evaluation. We developed an anonymous online form, with several questions about different general aspects, shared by all subjects, such as theory, practice, evaluation, etc., and a final question to get the general evaluation of the subject. Each question must be answered with a numerical value between 1 and 5; the student can introduce text to explain or justify the numerical value. The proposed form is sufficiently general to be applied to any subject. Still, at the same time, with the part of text answers that accompanies each question, plus the general open question, the form allows the singularities of each subject to be considered. The objective is to know the student's opinion about some aspects of subjects through a general question. Concretely, the system aims at understanding all that a student wants to outline about the subject, good or bad, and the possible correlation or not between numerical and text responses. The proposed system automatically identifies the sentiment of text answers through Natural Language Processing techniques. We collected student feedback for four different subjects from 2-degree programs. First, we could see that approximately 40% of the enrolled students responded to the form. In the case of the university's institutional surveys, they are usually done by more students because they are mandatory, but this means that the answers they give could be not sincere. On the other hand, we have taken as a gold standard the numerical response of the students to the open-ended question about the general assessment, and we have contrasted it with the results of the automatic sentiment analysis. A significant correlation was found for negative evaluations, whereas the less was for positive ones. The students indicated that they liked the subject according to the numerical evaluation but did not express the same in the text response. Consequently, it can indicate two things: either that the sentiment analyzer needs to be adjusted or that the students are inconsistent in their answers. Further work will be done on the sentiment analysis system, reviewing the vocabulary used by the students, extending the study to all the questions on the form, and analyzing whether the students' opinions on the overall rating are different from those stated on the form itself.
Performance evaluation of a 3D multi-view-based particle filter for visual object tracking using GPUs and multicore CPUs
(Journal of Real-Time Image Processing, 2018-08-01) Concha, David; Cabido, Raúl; Pantrigo, Juan José; Sanz, Antonio
This paper presents a deep and extensive performance analysis of the particle filter (PF) algorithm for a very compute intensive 3D multi-view visual tracking problem. We compare different implementations and parameter settings of the PF algorithm in a CPU platform taking advantage of the multithreading capabilities of the modern processors and a graphics processing unit (GPU) platform using NVIDIA CUDA computing environment as developing framework. We extend our experimental study to each individual stage of the PF algorithm, and evaluate the quality versus performance trade-off among different ways to design these stages. We have observed that the GPU platform performs better than the multithreaded CPU platform when handling a large number of particles, but we also demonstrate that hybrid CPU/GPU implementations can run almost as fast as only GPU solutions.