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Author Jose Manuel Alvarez; Ferran Diego; Joan Serrat; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Automatic Ground-truthing using video registration for on-board detection algorithms Type Conference Article
  Year 2009 Publication 16th IEEE International Conference on Image Processing Abbreviated Journal  
  Volume Issue Pages 4389 - 4392  
  Keywords  
  Abstract Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate.  
  Address Cairo, Egypt  
  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 1522-4880 ISBN 978-1-4244-5653-6 Medium  
  Area Expedition Conference ICIP  
  Notes ADAS Approved no  
  Call Number (up) ADAS @ adas @ ADS2009 Serial 1201  
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Author Jose Manuel Alvarez; Theo Gevers; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Learning Photometric Invariance from Diversified Color Model Ensembles Type Conference Article
  Year 2009 Publication 22nd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 565–572  
  Keywords road detection  
  Abstract Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively 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, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may 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 taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition.  
  Address Miami (USA)  
  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 1063-6919 ISBN 978-1-4244-3992-8 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS;ISE Approved no  
  Call Number (up) ADAS @ adas @ AGL2009 Serial 1169  
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Author Jose Manuel Alvarez; Theo Gevers; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title 3D Scene Priors for Road Detection Type Conference Article
  Year 2010 Publication 23rd IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 57–64  
  Keywords road detection  
  Abstract Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods.  
  Address San Francisco; CA; USA; June 2010  
  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 1063-6919 ISBN 978-1-4244-6984-0 Medium  
  Area Expedition Conference CVPR  
  Notes ADAS;ISE Approved no  
  Call Number (up) ADAS @ adas @ AGL2010a Serial 1302  
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Author Jaume Amores; David Geronimo; Antonio Lopez edit   pdf
openurl 
  Title Multiple instance and active learning for weakly-supervised object-class segmentation Type Conference Article
  Year 2010 Publication 3rd IEEE International Conference on Machine Vision Abbreviated Journal  
  Volume Issue Pages  
  Keywords Multiple Instance Learning; Active Learning; Object-class segmentation.  
  Abstract In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set.
 
  Address Hong-Kong  
  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 ICMV  
  Notes ADAS Approved no  
  Call Number (up) ADAS @ adas @ AGL2010b Serial 1429  
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Author Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich edit   pdf
openurl 
  Title Shadow Resistant Road Segmentation from a Mobile Monocular System Type Conference Article
  Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16 Abbreviated Journal  
  Volume Issue Pages  
  Keywords road detection  
  Abstract  
  Address Gerona (Spain)  
  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;CIC Approved no  
  Call Number (up) ADAS @ adas @ ALB2007 Serial 943  
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Author Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich edit   pdf
openurl 
  Title Illuminant Invariant Model-Based Road Segmentation Type Conference Article
  Year 2008 Publication IEEE Intelligent Vehicles Symposium, Abbreviated Journal  
  Volume Issue Pages 1155–1180  
  Keywords road detection  
  Abstract  
  Address Eindhoven (The Netherlands)  
  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;CIC Approved no  
  Call Number (up) ADAS @ adas @ ALB2008 Serial 1045  
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Author Jose Manuel Alvarez; Felipe Lumbreras; Theo Gevers; Antonio Lopez edit   pdf
url  doi
openurl 
  Title Geographic Information for vision-based Road Detection Type Conference Article
  Year 2010 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal  
  Volume Issue Pages 621–626  
  Keywords road detection  
  Abstract Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach.  
  Address San Diego; CA; USA  
  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 IV  
  Notes ADAS;ISE Approved no  
  Call Number (up) ADAS @ adas @ ALG2010 Serial 1428  
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Author Jose Manuel Alvarez; Antonio Lopez edit   pdf
openurl 
  Title Novel Index for Objective Evaluation of Road Detection Algorithms Type Conference Article
  Year 2008 Publication Intelligent Transportation Systems. 11th International IEEE Conference on, Abbreviated Journal  
  Volume Issue Pages 815–820  
  Keywords road detection  
  Abstract  
  Address Beijing (Xina)  
  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 ITSC  
  Notes ADAS Approved no  
  Call Number (up) ADAS @ adas @ AlL2008 Serial 1074  
Permanent link to this record
 

 
Author Jaume Amores edit  doi
isbn  openurl
  Title Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study Type Conference Article
  Year 2010 Publication 20th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 4246–4250  
  Keywords  
  Abstract Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families, and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that, with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases, showing in general a more consistent performance.  
  Address Istanbul, Turkey  
  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 1051-4651 ISBN 978-1-4244-7542-1 Medium  
  Area Expedition Conference ICPR  
  Notes ADAS Approved no  
  Call Number (up) ADAS @ adas @ Amo2010 Serial 1295  
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Author Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez edit   pdf
openurl 
  Title Comparison of two non-linear model-based control strategies for autonomous vehicles Type Conference Article
  Year 2016 Publication 24th Mediterranean Conference on Control and Automation Abbreviated Journal  
  Volume Issue Pages 846-851  
  Keywords Autonomous Driving; Control  
  Abstract This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation.  
  Address Athens; Greece; June 2016  
  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 MED  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number (up) ADAS @ adas @ ASP2016 Serial 2750  
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