| Records |
| Author |
Kevin E. Muñoz Loberlly N. Salazar Steven S. Araujo Boris X. Vintimilla |
| Title |
Detecting and Characterizing Human Interactions to EnhanceHuman-Robot Engagement |
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Conference Article |
| Year |
2025 |
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3rd International Conference on Robotics, Control and Vision Engineering RCVE 2025 |
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no |
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cidis @ cidis @ |
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281 |
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| Author |
Marjorie Chalen; Boris X. Vintimilla |
| Title |
Towards Action Prediction Applying Deep Learning |
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Journal Article |
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2019 |
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Latin American Conference on Computational Intelligence (LA-CCI); Guayaquil, Ecuador; 11-15 Noviembre 2019 |
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pp. 1-3 |
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action prediction, early recognition, early detec- tion, action anticipation, cnn, deep learning, rnn, lstm. |
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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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cidis @ cidis @ |
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129 |
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| Author |
Ricaurte P; Chilán C; Cristhian A. Aguilera; Boris X. Vintimilla; Angel D. Sappa |
| Title |
Feature Point Descriptors: Infrared and Visible Spectra |
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Journal Article |
| Year |
2014 |
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Sensors Journal |
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Vol. 14 |
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pp. 3690-3701 |
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cross-spectral imaging; feature point descriptors |
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This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given. |
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English |
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cidis @ cidis @ |
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28 |
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Patricia L. Suárez, Angel D. Sappa, Boris X. Vintimilla |
| Title |
Cycle generative adversarial network: towards a low-cost vegetation index estimation |
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Conference Article |
| Year |
2021 |
Publication |
IEEE International Conference on Image Processing (ICIP 2021) |
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2021-September |
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2783-2787 |
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CyclicGAN, NDVI, near infrared spectra, instance normalization. |
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This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI).The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach. |
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cidis @ cidis @ |
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164 |
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| Author |
Dennis G. Romero; A. Frizera; Angel D. Sappa; Boris X. Vintimilla; T.F. Bastos |
| Title |
A predictive model for human activity recognition by observing actions and context |
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Conference Article |
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2015 |
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ACIVS 2015 (Advanced Concepts for Intelligent Vision Systems), International Conference on, Catania, Italy, 2015 |
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323 - 333 |
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Edge width, Image blu,r Defocus map, Edge model |
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This paper presents a novel model to estimate human activities – a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach. |
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cidis @ cidis @ |
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43 |
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| Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
| Title |
Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles |
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Journal Article |
| Year |
2016 |
Publication |
Journal of Automation and Control Engineering (JOACE) |
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Vol. 4 |
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pp. 430-436 |
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Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System |
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Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset. |
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English |
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cidis @ cidis @ |
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51 |
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| Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
| Title |
A Fault Tolerant Perception system for autonomous vehicles |
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Conference Article |
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2016 |
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35th Chinese Control Conference (CCC2016), International Conference on, Chengdu |
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1-6 |
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Fault Tolerant Perception, Sensor Data Fusion, Fault Tolerance, Autonomous Vehicles, Federated Architecture |
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Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset. |
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cidis @ cidis @ |
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52 |
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| Author |
Dennis G. Romero; A. F. Neto; T. F. Bastos; Boris X. Vintimilla |
| Title |
RWE patterns extraction for on-line human action recognition through window-based analysis of invariant moments |
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Conference Article |
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2012 |
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5th Workshop in applied Robotics and Automation (RoboControl) |
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Human action recognition, Relative Wavelet Energy, Window-based temporal analysis. |
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This paper presents a method for on-line human action recognition on video sequences. An analysis based on Mahalanobis distance is performed to identify the “idle” state, which defines the beginning and end of the person movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments. |
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cidis @ cidis @ |
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23 |
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| Author |
Angel D. Sappa; Juan A. Carvajal; Cristhian A. Aguilera; Miguel Oliveira; Dennis G. Romero; Boris X. Vintimilla |
| Title |
Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study |
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Journal Article |
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2016 |
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Sensors Journal |
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Vol. 16 |
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pp. 1-15 |
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image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform |
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This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and LongWave InfraRed (LWIR). |
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cidis @ cidis @ |
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47 |
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Angel D. Sappa; Cristhian A. Aguilera; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo |
| Title |
Monocular visual odometry: a cross-spectral image fusion based approach |
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Journal Article |
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2016 |
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Robotics and Autonomous Systems Journal |
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Vol. 86 |
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pp. 26-36 |
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Monocular visual odometry LWIR-RGB cross-spectral imaging Image fusion |
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This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is em- pirically obtained by means of a mutual information based evaluation met- ric. The objective is to have a exible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odom- etry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. |
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cidis @ cidis @ |
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54 |
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