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Author Joan Serrat; Ferran Diego; Felipe Lumbreras edit  openurl
  Title (down) Los faros delanteros a traves del objetivo Type Journal
  Year 2008 Publication UAB Divulga, Revista de divulgacion cientifica Abbreviated Journal  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SDL2008b Serial 1471  
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Author Joan Serrat; Felipe Lumbreras; Idoia Ruiz edit  url
openurl 
  Title (down) Learning to measure for preshipment garment sizing Type Journal Article
  Year 2018 Publication Measurement Abbreviated Journal MEASURE  
  Volume 130 Issue Pages 327-339  
  Keywords Apparel; Computer vision; Structured prediction; Regression  
  Abstract Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively.  
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  Notes ADAS: MSIAU; 600.122; 600.118 Approved no  
  Call Number Admin @ si @ SLR2018 Serial 3128  
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Author Jose Manuel Alvarez; Theo Gevers; Antonio Lopez edit   pdf
doi  openurl
  Title (down) Learning photometric invariance for object detection Type Journal Article
  Year 2010 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 90 Issue 1 Pages 45-61  
  Keywords road detection  
  Abstract Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile
Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.
Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.
Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods		  
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  Publisher Springer US Place of Publication Editor  
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  ISSN 0920-5691 ISBN Medium  
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  Notes ADAS;ISE Approved no  
  Call Number ADAS @ adas @ AGL2010c Serial 1451  
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Author Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa edit   pdf
doi  isbn
openurl 
  Title (down) Learning a Part-based Pedestrian Detector in Virtual World Type Journal Article
  Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 15 Issue 5 Pages 2121-2131  
  Keywords Domain Adaptation; Pedestrian Detection; Virtual Worlds  
  Abstract Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM.  
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  ISSN 1931-0587 ISBN 978-1-4673-2754-1 Medium  
  Area Expedition Conference  
  Notes ADAS; 600.076 Approved no  
  Call Number ADAS @ adas @ XVL2014 Serial 2433  
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Author Ferran Diego; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title (down) Joint spatio-temporal alignment of sequences Type Journal Article
  Year 2013 Publication IEEE Transactions on Multimedia Abbreviated Journal TMM  
  Volume 15 Issue 6 Pages 1377-1387  
  Keywords video alignment  
  Abstract Video alignment is important in different areas of computer vision such as wide baseline matching, action recognition, change detection, video copy detection and frame dropping prevention. Current video alignment methods usually deal with a relatively simple case of fixed or rigidly attached cameras or simultaneous acquisition. Therefore, in this paper we propose a joint video alignment for bringing two video sequences into a spatio-temporal alignment. Specifically, the novelty of the paper is to formulate the video alignment to fold the spatial and temporal alignment into a single alignment framework. This simultaneously satisfies a frame-correspondence and frame-alignment similarity; exploiting the knowledge among neighbor frames by a standard pairwise Markov random field (MRF). This new formulation is able to handle the alignment of sequences recorded at different times by independent moving cameras that follows a similar trajectory, and also generalizes the particular cases that of fixed geometric transformation and/or linear temporal mapping. We conduct experiments on different scenarios such as sequences recorded simultaneously or by moving cameras to validate the robustness of the proposed approach. The proposed method provides the highest video alignment accuracy compared to the state-of-the-art methods on sequences recorded from vehicles driving along the same track at different times.  
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  ISSN 1520-9210 ISBN Medium  
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  Notes ADAS Approved no  
  Call Number Admin @ si @ DSL2013; ADAS @ adas @ Serial 2228  
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Author Zhijie Fang; Antonio Lopez edit  url
doi  openurl
  Title (down) Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation Type Journal Article
  Year 2019 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
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  Abstract Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs.  
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  Notes ADAS; no proj Approved no  
  Call Number Admin @ si @ FaL2019 Serial 3305  
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Author Fadi Dornaika; Angel Sappa edit  doi
openurl 
  Title (down) Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression Type Journal Article
  Year 2009 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 30 Issue 5 Pages 535–543  
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  Abstract This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes.  
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  Publisher Elsevier Science Inc. Place of Publication Editor  
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  ISSN 0167-8655 ISBN Medium  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DoS2009a Serial 1115  
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Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira edit   pdf
url  openurl
  Title (down) Incremental texture mapping for autonomous driving Type Journal Article
  Year 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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  Notes ADAS; 600.086 Approved no  
  Call Number Admin @ si @ OSS2016b Serial 2912  
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Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira edit   pdf
doi  openurl
  Title (down) Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives Type Journal Article
  Year 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.  
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  Publisher Elsevier B.V. Place of Publication Editor  
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  Notes ADAS; 600.086, 600.076 Approved no  
  Call Number Admin @ si @OSS2016a Serial 2806  
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Author Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon edit  url
openurl 
  Title (down) Incremental model learning for spectroscopy-based food analysis Type Journal Article
  Year 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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  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ DGK2017 Serial 3002  
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