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Author Victor Santos; Angel Sappa; Miguel Oliveira edit  openurl
  Title Special Issue on Autonomous Driving and Driver Assistance Systems Type Journal Article
  Year 2017 Publication (up) Robotics and Autonomous Systems Abbreviated Journal RAS  
  Volume 91 Issue Pages 208-209  
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  Area Expedition Conference  
  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ SSO2017 Serial 2915  
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Author Cristhian Aguilera; Fernando Barrera; Felipe Lumbreras; Angel Sappa; Ricardo Toledo edit   pdf
doi  openurl
  Title Multispectral Image Feature Points Type Journal Article
  Year 2012 Publication (up) Sensors Abbreviated Journal SENS  
  Volume 12 Issue 9 Pages 12661-12672  
  Keywords multispectral image descriptor; color and infrared images; feature point descriptor  
  Abstract Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ ABL2012 Serial 2154  
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Author P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris Vintimilla; Angel Sappa edit  doi
openurl 
  Title Feature Point Descriptors: Infrared and Visible Spectra Type Journal Article
  Year 2014 Publication (up) Sensors Abbreviated Journal SENS  
  Volume 14 Issue 2 Pages 3690-3701  
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  Abstract 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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  Notes ADAS;600.055; 600.076 Approved no  
  Call Number Admin @ si @ RCA2014a Serial 2474  
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Author Alejandro Gonzalez Alzate; Zhijie Fang; Yainuvis Socarras; Joan Serrat; David Vazquez; Jiaolong Xu; Antonio Lopez edit   pdf
doi  openurl
  Title Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison Type Journal Article
  Year 2016 Publication (up) Sensors Abbreviated Journal SENS  
  Volume 16 Issue 6 Pages 820  
  Keywords Pedestrian Detection; FIR  
  Abstract Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and night time. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images, (b) just infrared images and (c) both of them. In order to obtain results for the last item we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset we have built for this purpose as well as on the publicly available KAIST multispectral dataset.  
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  Series Volume Series Issue Edition  
  ISSN 1424-8220 ISBN Medium  
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  Notes ADAS; 600.085; 600.076; 600.082; 601.281 Approved no  
  Call Number ADAS @ adas @ GFS2016 Serial 2754  
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Author Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris Vintimilla edit   pdf
doi  openurl
  Title Wavelet based visible and infrared image fusion: a comparative study Type Journal Article
  Year 2016 Publication (up) Sensors Abbreviated Journal SENS  
  Volume 16 Issue 6 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 Long Wave InfraRed (LWIR).  
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  Notes ADAS; 600.086; 600.076 Approved no  
  Call Number Admin @ si @SCA2016 Serial 2807  
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Author Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo edit  doi
openurl 
  Title Cross-Spectral Local Descriptors via Quadruplet Network Type Journal Article
  Year 2017 Publication (up) Sensors Abbreviated Journal SENS  
  Volume 17 Issue 4 Pages 873  
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  Abstract This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.  
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  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ ASA2017 Serial 2914  
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Author Zhijie Fang; David Vazquez; Antonio Lopez edit  doi
openurl 
  Title On-Board Detection of Pedestrian Intentions Type Journal Article
  Year 2017 Publication (up) Sensors Abbreviated Journal SENS  
  Volume 17 Issue 10 Pages 2193  
  Keywords pedestrian intention; ADAS; self-driving  
  Abstract Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors.
However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is
essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the
pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information.
 
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  Notes ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 Approved no  
  Call Number Admin @ si @ FVL2017 Serial 2983  
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Author Xavier Soria; Angel Sappa; Riad I. Hammoud edit  url
doi  openurl
  Title Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images Type Journal Article
  Year 2018 Publication (up) Sensors Abbreviated Journal SENS  
  Volume 18 Issue 7 Pages 2059  
  Keywords RGB-NIR sensor; multispectral imaging; deep learning; CNNs  
  Abstract Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches.
 
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  Notes ADAS: MSIAU; 600.086; 600.130; 600.122; 600.118 Approved no  
  Call Number Admin @ si @ SSH2018 Serial 3145  
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Author Gabriel Villalonga; Joost van de Weijer; Antonio Lopez edit  url
doi  openurl
  Title Recognizing new classes with synthetic data in the loop: application to traffic sign recognition Type Journal Article
  Year 2020 Publication (up) Sensors Abbreviated Journal SENS  
  Volume 20 Issue 3 Pages 583  
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  Abstract On-board vision systems may need to increase the number of classes that can be recognized in a relatively short period. For instance, a traffic sign recognition system may suddenly be required to recognize new signs. Since collecting and annotating samples of such new classes may need more time than we wish, especially for uncommon signs, we propose a method to generate these samples by combining synthetic images and Generative Adversarial Network (GAN) technology. In particular, the GAN is trained on synthetic and real-world samples from known classes to perform synthetic-to-real domain adaptation, but applied to synthetic samples of the new classes. Using the Tsinghua dataset with a synthetic counterpart, SYNTHIA-TS, we have run an extensive set of experiments. The results show that the proposed method is indeed effective, provided that we use a proper Convolutional Neural Network (CNN) to perform the traffic sign recognition (classification) task as well as a proper GAN to transform the synthetic images. Here, a ResNet101-based classifier and domain adaptation based on CycleGAN performed extremely well for a ratio∼ 1/4 for new/known classes; even for more challenging ratios such as∼ 4/1, the results are also very positive.  
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  Notes LAMP; ADAS; no proj Approved no  
  Call Number Admin @ si @ VWL2020 Serial 3405  
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Author A. Restrepo; Angel Sappa; M. Devy edit  url
openurl 
  Title Edge registration versus triangular mesh registration, a comparative study Type Journal
  Year 2005 Publication (up) Signal Processing: Image Communication 20: 853–868 (IF: 1.264) Abbreviated Journal  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ RSD2005 Serial 567  
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