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Author (up) Angel D. Sappa; Juan A. Carvajal; Cristhian A. Aguilera; Miguel Oliveira; Dennis G. Romero; Boris X. Vintimilla pdf  url
openurl 
  Title Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study Type Journal Article
  Year 2016 Publication Sensors Journal Abbreviated Journal  
  Volume vol 16 Issue Pages 1-15  
  Keywords image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform  
  Abstract 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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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 47  
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Author (up) Angel D. Sappa; Cristhian A. Aguilera; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo pdf  url
doi  openurl
  Title Monocular visual odometry: a cross-spectral image fusion based approach Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal  
  Volume vol 86 Issue Pages 26-36  
  Keywords Monocular visual odometry LWIR-RGB cross-spectral imaging Image fusion  
  Abstract 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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  Language Enlgish Summary Language English Original Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 54  
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Author (up) Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo pdf  openurl
  Title Learning crossspectral similarity measures with deep convolutional neural networks Type Conference Article
  Year 2016 Publication IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains.  
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  Language English Summary Language English Original Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 48  
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Author (up) Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa pdf  openurl
  Title Fine-tuning based deep covolutional networks for lepidopterous genus recognition Type Conference Article
  Year 2016 Publication XXI IberoAmerican Congress on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1-9  
  Keywords  
  Abstract This paper describes an image classi cation approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio  
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  Call Number cidis @ cidis @ Serial 53  
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Author (up) Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira pdf  url
openurl 
  Title Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal  
  Volume 83 Issue Pages 312-325  
  Keywords Incremental scene reconstructionPoint cloudsAutonomous vehiclesPolygonal primitives  
  Abstract When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.  
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  Language English Summary Language English Original Title  
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  Notes Approved no  
  Call Number cidis @ cidis @ Serial 49  
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Author (up) Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias; A. Paulo Moreira pdf  url
openurl 
  Title Incremental Texture Mapping for Autonomous Driving Type Journal Article
  Year 2016 Publication Robotics and Autonomous Systems Journal Abbreviated Journal  
  Volume 84 Issue Pages 113-128  
  Keywords Scene reconstruction, Autonomous driving, Texture mapping  
  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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  Language English Summary Language English Original Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 50  
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Author (up) Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic pdf  openurl
  Title A Fault Tolerant Perception system for autonomous vehicles Type Conference Article
  Year 2016 Publication 35th Chinese Control Conference (CCC2016), International Conference on, Chengdu Abbreviated Journal  
  Volume Issue Pages 1-6  
  Keywords Fault Tolerant Perception, Sensor Data Fusion, Fault Tolerance, Autonomous Vehicles, Federated Architecture  
  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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  Language English Summary Language English Original Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 52  
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Author (up) Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic pdf  openurl
  Title Multi-sensor Fusion Module in a Fault Tolerant Perception System for Autonomous Vehicles Type Journal Article
  Year 2016 Publication Journal of Automation and Control Engineering (JOACE) Abbreviated Journal  
  Volume 4 Issue Pages 430-436  
  Keywords Fault Tolerance, Data Fusion, Multi-sensor Fusion, Autonomous Vehicles, Perception System  
  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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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number cidis @ cidis @ Serial 51  
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