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Author Antonio Lopez; Gabriel Villalonga; Laura Sellart; German Ros; David Vazquez; Jiaolong Xu; Javier Marin; Azadeh S. Mozafari edit  url
openurl 
  Title Training my car to see using virtual worlds Type Journal Article
  Year (down) 2017 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 38 Issue Pages 102-118  
  Keywords  
  Abstract Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks that it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting of training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance.  
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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ LVS2017 Serial 2985  
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Author Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon edit  url
openurl 
  Title Incremental model learning for spectroscopy-based food analysis Type Journal Article
  Year (down) 2017 Publication Chemometrics and Intelligent Laboratory Systems Abbreviated Journal CILS  
  Volume 167 Issue Pages 123-131  
  Keywords Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy  
  Abstract In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps.  
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  Area Expedition Conference  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ DGK2017 Serial 3002  
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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate edit  url
openurl 
  Title Decremental generalized discriminative common vectors applied to images classification Type Journal Article
  Year (down) 2017 Publication Knowledge-Based Systems Abbreviated Journal KBS  
  Volume 131 Issue Pages 46-57  
  Keywords Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification  
  Abstract In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model.  
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  Notes DAG; ADAS; 600.118; 600.121 Approved no  
  Call Number Admin @ si @ DMH2017a Serial 3003  
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Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez edit   pdf
doi  openurl
  Title Hierarchical Adaptive Structural SVM for Domain Adaptation Type Journal Article
  Year (down) 2016 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 119 Issue 2 Pages 159-178  
  Keywords Domain Adaptation; Pedestrian Detection  
  Abstract A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition.
 
  Address  
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  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number Admin @ si @ XRV2016 Serial 2669  
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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 (down) 2016 Publication 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 Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1424-8220 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.085; 600.076; 600.082; 601.281 Approved no  
  Call Number ADAS @ adas @ GFS2016 Serial 2754  
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Author Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez edit   pdf
doi  openurl
  Title A reduced feature set for driver head pose estimation Type Journal Article
  Year (down) 2016 Publication Applied Soft Computing Abbreviated Journal ASOC  
  Volume 45 Issue Pages 98-107  
  Keywords Head pose estimation; driving performance evaluation; subspace based methods; linear regression  
  Abstract Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application.  
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  Notes ADAS; 600.085; 600.076; Approved no  
  Call Number Admin @ si @ DHL2016 Serial 2760  
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Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira edit   pdf
doi  openurl
  Title Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives Type Journal Article
  Year (down) 2016 Publication Robotics and Autonomous Systems Abbreviated Journal RAS  
  Volume 83 Issue Pages 312-325  
  Keywords Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal 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.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier B.V. Place of Publication Editor  
  Language Summary Language Original Title  
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  Notes ADAS; 600.086, 600.076 Approved no  
  Call Number Admin @ si @OSS2016a Serial 2806  
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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 (down) 2016 Publication 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 Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris Vintimilla; Ricardo Toledo edit   pdf
doi  openurl
  Title Monocular visual odometry: A cross-spectral image fusion based approach Type Journal Article
  Year (down) 2016 Publication Robotics and Autonomous Systems Abbreviated Journal RAS  
  Volume 85 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 empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry 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.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier B.V. Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
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  Area Expedition Conference  
  Notes ADAS;600.086; 600.076 Approved no  
  Call Number Admin @ si @SAC2016 Serial 2811  
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Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira edit   pdf
url  openurl
  Title Incremental texture mapping for autonomous driving Type Journal Article
  Year (down) 2016 Publication Robotics and Autonomous Systems Abbreviated Journal RAS  
  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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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.086 Approved no  
  Call Number Admin @ si @ OSS2016b Serial 2912  
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