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Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Fast Spatial Pattern Discovery Integrating Boosting with Constellations of Contextual Descriptors |
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Miscellaneous |
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2005 |
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IEEE Computer Society, International Conference on Computer Vision and Pattern Recognition (CVPR’05), 2(2):769–774 |
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San Diego, CA (USA) |
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MILAB |
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ADAS @ adas @ ASR2005a |
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541 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva; Theo Gevers; A. Smeulders |
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Title |
Boosting Contextual Information in Content-based Image Retrieval |
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Miscellaneous |
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2004 |
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6th ACM SIGMM International Workshop on Multimedia Information Retrieval |
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New York, USA |
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MILAB;ISE |
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ADAS @ adas @ ASR2004 |
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466 |
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Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez |
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Title |
Comparison of two non-linear model-based control strategies for autonomous vehicles |
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2016 |
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24th Mediterranean Conference on Control and Automation |
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846-851 |
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Autonomous Driving; Control |
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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. |
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Athens; Greece; June 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ ASP2016 |
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2750 |
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Author |
Niki Aifanti; Angel Sappa; N. Grammalidis; Sotiris Malassiotis |
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Title |
Advances in Tracking and Recognition of Human Motion |
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2009 |
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Encyclopedia of Information Science and Technology |
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I |
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2nd edition |
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65–71 |
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ADAS @ adas @ ASG2009 |
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1143 |
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Niki Aifanti; Angel Sappa; N. Grammalidis; Sotiris Malassiotis |
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Title |
Human Motion Tracking and Recognition |
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2005 |
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Encyclopedia of Information Science and Technology, 1(5):1355–1360 |
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ADAS @ adas @ ASG2005 |
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496 |
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Author |
Jaume Amores; Petia Radeva |
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Title |
Registration and Retrieval of Highly Elastic Bodies using Contextual Information |
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Journal Article |
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Year |
2005 |
Publication |
Pattern Recognition Letters |
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PRL |
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26 |
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11 |
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1720–1731 |
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IF: 1.138 |
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ADAS;MILAB |
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ADAS @ adas @ AmR2005b |
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592 |
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Author |
Jaume Amores; Petia Radeva |
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Title |
Retrieval of IVUS Images Using Contextual Information and Elastic Matching |
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2005 |
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International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657) |
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ADAS;MILAB |
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ADAS @ adas @ AmR2005a |
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593 |
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Author |
Jaume Amores; Petia Radeva |
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Title |
Registration and retrieval of medical images. Application to IVUS |
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Report |
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2004 |
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CVC Technical Report #77 |
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CVC (UAB) |
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MILAB |
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ADAS @ adas @ AmR2004 |
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513 |
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Author |
Jaume Amores; Petia Radeva |
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Title |
Elastic Matching and Retrieval of IVUS Images Using Contextual Information |
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Miscellaneous |
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2003 |
Publication |
6th Catalan Conference on Artificial Intelligence (CCIA 2003), 137–148 |
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Palma de Mallorca |
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ADAS;MILAB |
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ADAS @ adas @ AmR2003a |
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364 |
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Author |
Jaume Amores; Petia Radeva |
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Title |
Non-rigid Registration of Vessel Structures in IVUS Images |
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2003 |
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In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science. 2652:45–52 |
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Springer-Verlag |
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ADAS;MILAB |
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ADAS @ adas @ AmR2003 |
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363 |
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Author |
Jaume Amores |
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Title |
Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study |
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Conference Article |
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2010 |
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20th International Conference on Pattern Recognition |
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4246–4250 |
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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. |
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Istanbul, Turkey |
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1051-4651 |
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978-1-4244-7542-1 |
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ICPR |
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ADAS |
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ADAS @ adas @ Amo2010 |
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1295 |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
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Title |
Road Detection Based on Illuminant Invariance |
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2011 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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12 |
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1 |
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184-193 |
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road detection |
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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. |
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ADAS @ adas @ AlL2011 |
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1456 |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
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Novel Index for Objective Evaluation of Road Detection Algorithms |
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Conference Article |
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2008 |
Publication |
Intelligent Transportation Systems. 11th International IEEE Conference on, |
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815–820 |
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road detection |
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Beijing (Xina) |
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ITSC |
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ADAS @ adas @ AlL2008 |
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1074 |
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Author |
Jose Manuel Alvarez; Felipe Lumbreras; Theo Gevers; Antonio Lopez |
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Title |
Geographic Information for vision-based Road Detection |
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2010 |
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IEEE Intelligent Vehicles Symposium |
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621–626 |
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road detection |
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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. |
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San Diego; CA; USA |
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IV |
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ADAS @ adas @ ALG2010 |
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1428 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
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Illuminant Invariant Model-Based Road Segmentation |
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2008 |
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IEEE Intelligent Vehicles Symposium, |
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1155–1180 |
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road detection |
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Eindhoven (The Netherlands) |
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ADAS;CIC |
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ADAS @ adas @ ALB2008 |
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1045 |
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