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Author (down) 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 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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; ADAS; 600.118; 600.121 Approved no  
  Call Number Admin @ si @ DMH2017a Serial 3003  
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Author (down) Katerine Diaz; Francesc J. Ferri; W. Diaz edit  doi
openurl 
  Title Incremental Generalized Discriminative Common Vectors for Image Classification Type Journal Article
  Year 2015 Publication IEEE Transactions on Neural Networks and Learning Systems Abbreviated Journal TNNLS  
  Volume 26 Issue 8 Pages 1761 - 1775  
  Keywords  
  Abstract Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2162-237X ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ DFD2015 Serial 2547  
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Author (down) Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate edit  url
doi  openurl
  Title An overview of incremental feature extraction methods based on linear subspaces Type Journal Article
  Year 2018 Publication Knowledge-Based Systems Abbreviated Journal KBS  
  Volume 145 Issue Pages 219-235  
  Keywords  
  Abstract With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0950-7051 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ DFH2018 Serial 3090  
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Author (down) 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 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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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.085; 600.076; Approved no  
  Call Number Admin @ si @ DHL2016 Serial 2760  
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Author (down) Jose Manuel Alvarez; Theo Gevers; Ferran Diego; Antonio Lopez edit   pdf
doi  openurl
  Title Road Geometry Classification by Adaptative Shape Models Type Journal Article
  Year 2013 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 14 Issue 1 Pages 459-468  
  Keywords road detection  
  Abstract Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS;ISE Approved no  
  Call Number Admin @ si @ AGD2013;; ADAS @ adas @ Serial 2269  
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Author (down) Jose Manuel Alvarez; Theo Gevers; Antonio Lopez edit   pdf
doi  openurl
  Title 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
 
  Address  
  Corporate Author Thesis  
  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;ISE Approved no  
  Call Number ADAS @ adas @ AGL2010c Serial 1451  
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Author (down) Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras edit   pdf
doi  openurl
  Title Combining Priors, Appearance and Context for Road Detection Type Journal Article
  Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 15 Issue 3 Pages 1168-1178  
  Keywords Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout  
  Abstract Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning.
Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.
 
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.076;ISE Approved no  
  Call Number Admin @ si @ ALG2014 Serial 2501  
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Author (down) Jose Manuel Alvarez; Antonio Lopez edit   pdf
openurl 
  Title Road Detection Based on Illuminant Invariance Type Journal Article
  Year 2011 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 12 Issue 1 Pages 184-193  
  Keywords road detection  
  Abstract By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms.  
  Address  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ AlL2011 Serial 1456  
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Author (down) Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa edit   pdf
url  doi
openurl 
  Title Multiple target tracking for intelligent headlights control Type Journal Article
  Year 2012 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 13 Issue 2 Pages 594-605  
  Keywords Intelligent Headlights  
  Abstract Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm.  
  Address  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1524-9050 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g Serial 1877  
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Author (down) Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira edit   pdf
openurl 
  Title Dynamic Comparison of Headlights Type Journal
  Year 2008 Publication Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering Abbreviated Journal  
  Volume 222 Issue 5 Pages 643–656  
  Keywords video alignment  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SDL2008a Serial 958  
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