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Author |
Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
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![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Harmony Potentials for Joint Classification and Segmentation |
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Conference Article |
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Year |
2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3280–3287 |
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Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS;CIC;ISE |
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ADAS @ adas @ GBW2010 |
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1296 |
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Author |
Josep Llados; Felipe Lumbreras; Javier Varona |
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Title |
A multidocument platform for automatic reading of identity cards. |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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Bilbao |
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ADAS;DAG |
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ADAS @ adas @ LLV1999 |
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7 |
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Author |
Josep Llados; Felipe Lumbreras; V. Chapaprieta; J. Queralt |
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Title |
ICAR: Identity Card Automatic Reader. |
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Miscellaneous |
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2001 |
Publication |
Sixth International Conference on Document Analysis and Recognition |
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ICDAR 2001 |
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470–474 |
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USA |
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ADAS @ adas @ LLC2001 |
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112 |
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Antonio Lopez; Ernest Valveny; Juan J. Villanueva |
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Title |
Real-time quality control of surgical material packaging by artificial vision |
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Journal Article |
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2005 |
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Assembly Automation |
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25 |
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3 |
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IF: 0.061) |
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ADAS;DAG |
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ADAS @ adas @ LVV2005 |
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552 |
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Author |
Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva |
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Title |
Colour Normalisation Based on Background Information. |
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Miscellaneous |
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2001 |
Publication |
Proceeding ICIP 2001, IEEE International Conference on Image Processing |
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ICIP 2001 |
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1 |
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874–877 |
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Grecia. |
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ADAS;DAG;CIC |
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ADAS @ adas @ VLP2001 |
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167 |
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Author |
Joan Serrat; Enric Marti |
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Title |
Elastic matching using interpolation splines |
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Conference Article |
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Year |
1991 |
Publication |
IV Spanish Symposium of Pattern Recognition and image Analysis |
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ADAS;IAM; |
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IAM @ iam @ SMV1991 |
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1651 |
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Author |
Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio |
![download PDF file pdf](img/file_PDF.gif)
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Title |
EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game |
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Conference Article |
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2016 |
Publication |
5th International Conference Games and Learning Alliance |
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10056 |
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50-59 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Abstract |
Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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GALA |
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ADAS;IAM; |
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HAC2016 |
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2864 |
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Author |
Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier |
![download PDF file pdf](img/file_PDF.gif)
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Title |
LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode |
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Conference Article |
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2016 |
Publication |
14th European Conference on Computer Vision Workshops |
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9915 |
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894-900 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Abstract |
Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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ADAS;IAM; 600.085; 600.076 |
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MHE2016 |
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2865 |
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Author |
Joan Serrat; Javier Varona; Antonio Lopez; Xavier Roca; Juan J. Villanueva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
P3: a three-dimensional digitizer prototype. |
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Miscellaneous |
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2001 |
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Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:315–322. |
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Castellon. |
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ADAS;ISE |
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ADAS @ adas @ SVL2001 |
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213 |
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Author |
Daniel Ponsa; Xavier Roca |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
A Novel Approach to Generate Multiple Shape Models. |
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Miscellaneous |
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2002 |
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Articulated Motion and Deformable Objects, LNCS 2492: 80–91, Springer Verlag. |
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ADAS;ISE |
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ADAS @ adas @ PoR2002a |
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283 |
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Author |
Daniel Ponsa; Xavier Roca |
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Title |
Unsupervised Parameterisation of Gaussian Mixture Models |
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Miscellaneous |
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2002 |
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ADAS;ISE |
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ADAS @ adas @ PoR2002c |
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313 |
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Author |
Daniel Ponsa; Xavier Roca |
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Title |
Multiple Model Approach to Deformable Shape Tracking |
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Miscellaneous |
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2003 |
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In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science 2652:782–792 |
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Springer-Verlag |
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ADAS @ adas @ PoR2003 |
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397 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Learning Photometric Invariance from Diversified Color Model Ensembles |
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Conference Article |
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2009 |
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22nd IEEE Conference on Computer Vision and Pattern Recognition |
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565–572 |
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road detection |
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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. |
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Miami (USA) |
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1063-6919 |
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978-1-4244-3992-8 |
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CVPR |
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ADAS @ adas @ AGL2009 |
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1169 |
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Author |
Xavier Boix; Josep M. Gonfaus; Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Marco Pedersoli; Jordi Gonzalez; Joan Serrat |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Combining local and global bag-of-word representations for semantic segmentation |
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Conference Article |
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2009 |
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Workshop on The PASCAL Visual Object Classes Challenge |
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Kyoto (Japan) |
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ICCV |
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ADAS;ISE |
Approved |
no |
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ADAS @ adas @ BGS2009 |
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1273 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
3D Scene Priors for Road Detection |
Type |
Conference Article |
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Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
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57–64 |
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road detection |
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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. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
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978-1-4244-6984-0 |
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CVPR |
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Approved |
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ADAS @ adas @ AGL2010a |
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1302 |
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