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Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
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Title |
Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles |
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2016 |
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Journal of Automation and Control Engineering (JOACE) |
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Vol. 4 |
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pp. 430-436 |
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Keywords |
Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System |
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Abstract |
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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51 |
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Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
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Title |
A Fault Tolerant Perception system for autonomous vehicles |
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Conference Article |
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Year |
2016 |
Publication |
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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Abstract |
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 |
Angel D. Sappa; Cristhian A. Aguilera; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo |
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Title |
Monocular visual odometry: a cross-spectral image fusion based approach |
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Journal Article |
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2016 |
Publication |
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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Author |
Angely Oyola; Dennis G. Romero; Boris X. Vintimilla |
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Title |
A Dijkstra-based algorithm for selecting the Shortest-Safe Evacuation Routes in dynamic environments (SSER) |
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2017 |
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The 30th International Conference on Industrial, Engineering, Other Applications of Applied Intelligent Systems (IEA/AIE 2017) |
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131-135 |
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cidis @ cidis @ |
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55 |
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Author |
Sianna Puente; Cindy Madrid; Miguel Realpe; Boris X. Vintimilla |
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Title |
An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System |
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2017 |
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2017 International Conference on Deep Learning Technologies (ICDLT 2017) |
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Part F128535 |
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60-65 |
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cidis @ cidis @ |
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56 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Cross-spectral Image Patch Similarity using Convolutional Neural Network |
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Conference Article |
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2017 |
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2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM) |
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1-5 |
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cidis @ cidis @ |
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57 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Learning to Colorize Infrared Images |
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Conference Article |
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2017 |
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15th International Conference on Practical Applications of Agents and Multi-Agent Systems |
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Call Number |
cidis @ cidis @ |
Serial |
58 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture. |
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Conference Article |
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2017 |
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13th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2017. (This paper has been selected as “Best Paper Award” ) |
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2017-July |
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212-217 |
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cidis @ cidis @ |
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62 |
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Author |
Marjorie Chalen; Boris X. Vintimilla |
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Title |
Towards Action Prediction Applying Deep Learning |
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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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Rafael E. Rivadeneira; Angel D. Sappa; Boris X. Vintimilla; Lin Guo; Jiankun Hou; Armin Mehri; Parichehr Behjati; Ardakani Heena Patel; Vishal Chudasama; Kalpesh Prajapati; Kishor P. Upla; Raghavendra Ramachandra; Kiran Raja; Christoph Busch; Feras Almasri; Olivier Debeir; Sabari Nathan; Priya Kansal; Nolan Gutierrez; Bardia Mojra; William J. Beksi |
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Thermal Image Super-Resolution Challenge – PBVS 2020 |
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2020 |
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The 16th IEEE Workshop on Perception Beyond the Visible Spectrum on the Conference on Computer Vision and Pattern Recongnition (CVPR 2020) |
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2020-June |
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9151059 |
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432-439 |
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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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21607508 |
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978-172819360-1 |
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cidis @ cidis @ |
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123 |
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