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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
A Fast accurate Implicit Polynomial Fitting Approach |
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Conference Article |
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Year |
2010 |
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17th IEEE International Conference on Image Processing |
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1429–1432 |
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This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons. |
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Hong-Kong |
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1522-4880 |
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978-1-4244-7992-4 |
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ICIP |
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ADAS @ adas @ RoS2010b |
Serial |
1359 |
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Author |
R. de Nijs; Sebastian Ramos; Gemma Roig; Xavier Boix; Luc Van Gool; K. Kühnlenz. |
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Title |
On-line Semantic Perception Using Uncertainty |
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Conference Article |
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Year |
2012 |
Publication |
International Conference on Intelligent Robots and Systems |
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IROS |
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4185-4191 |
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Keywords |
Semantic Segmentation |
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Visual perception capabilities are still highly unreliable in unconstrained settings, and solutions might not beaccurate in all regions of an image. Awareness of the uncertainty of perception is a fundamental requirement for proper high level decision making in a robotic system. Yet, the uncertainty measure is often sacrificed to account for dependencies between object/region classifiers. This is the case of Conditional Random Fields (CRFs), the success of which stems from their ability to infer the most likely world configuration, but they do not directly allow to estimate the uncertainty of the solution. In this paper, we consider the setting of assigning semantic labels to the pixels of an image sequence. Instead of using a CRF, we employ a Perturb-and-MAP Random Field, a recently introduced probabilistic model that allows performing fast approximate sampling from its probability density function. This allows to effectively compute the uncertainty of the solution, indicating the reliability of the most likely labeling in each region of the image. We report results on the CamVid dataset, a standard benchmark for semantic labeling of urban image sequences. In our experiments, we show the benefits of exploiting the uncertainty by putting more computational effort on the regions of the image that are less reliable, and use more efficient techniques for other regions, showing little decrease of performance |
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IROS |
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ADAS @ adas @ NRR2012 |
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2378 |
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Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Real-time Object Segmentation using a Bag of Features Approach |
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Conference Article |
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Year |
2010 |
Publication |
13th International Conference of the Catalan Association for Artificial Intelligence |
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220 |
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321–329 |
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Keywords |
Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors |
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In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset. |
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IOS Press Amsterdam, |
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In R.Alquezar, A.Moreno, J.Aguilar. |
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9781607506423 |
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CCIA |
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Call Number |
Admin @ si @ ARL2010b |
Serial |
1417 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
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Title |
Multiple-target tracking for the intelligent headlights control |
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Conference Article |
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Year |
2010 |
Publication |
13th Annual International Conference on Intelligent Transportation Systems |
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903–910 |
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Keywords |
Intelligent Headlights |
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Abstract |
TA7.4
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. |
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Madeira Island (Portugal) |
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ITSC |
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ADAS |
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no |
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ADAS @ adas @ RSL2010 |
Serial |
1422 |
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Author |
Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez |
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Title |
Vehicle geolocalization based on video synchronization |
Type |
Conference Article |
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Year |
2010 |
Publication |
13th Annual International Conference on Intelligent Transportation Systems |
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Pages |
1511–1516 |
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Keywords |
video alignment |
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Abstract |
TC8.6
This paper proposes a novel method for estimating the geospatial localization of a vehicle. I uses as input a georeferenced video sequence recorded by a forward-facing camera attached to the windscreen. The core of the proposed method is an on-line video synchronization which finds out the corresponding frame in the georeferenced video sequence to the one recorded at each time by the camera on a second drive through the same track. Once found the corresponding frame in the georeferenced video sequence, we transfer its geospatial information of this frame. The key advantages of this method are: 1) the increase of the update rate and the geospatial accuracy with regard to a standard low-cost GPS and 2) the ability to localize a vehicle even when a GPS is not available or is not reliable enough, like in certain urban areas. Experimental results for an urban environments are presented, showing an average of relative accuracy of 1.5 meters. |
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Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ADAS |
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Call Number |
ADAS @ adas @ DPS2010 |
Serial |
1423 |
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Author |
Ferran Diego; Jose Manuel Alvarez; Joan Serrat; Antonio Lopez |
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Title |
Vision-based road detection via on-line video registration |
Type |
Conference Article |
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Year |
2010 |
Publication |
13th Annual International Conference on Intelligent Transportation Systems |
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Pages |
1135–1140 |
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Keywords |
video alignment; road detection |
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Abstract |
TB6.2
Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following and vehicle and pedestrian detection. Significant efforts have been made in order to solve this task using vision-based techniques. The major challenge is to deal with lighting variations and the presence of objects on the road surface. In this paper, we propose a new road detection method to infer the areas of the image depicting road surfaces without performing any image segmentation. The idea is to previously segment manually or semi-automatically the road region in a traffic-free reference video record on a first drive. And then to transfer these regions to the frames of a second video sequence acquired later in a second drive through the same road, in an on-line manner. This is possible because we are able to automatically align the two videos in time and space, that is, to synchronize them and warp each frame of the first video to its corresponding frame in the second one. The geometric transform can thus transfer the road region to the present frame on-line. In order to reduce the different lighting conditions which are present in outdoor scenarios, our approach incorporates a shadowless feature space which represents an image in an illuminant-invariant feature space. Furthermore, we propose a dynamic background subtraction algorithm which removes the regions containing vehicles in the observed frames which are within the transferred road region. |
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Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ITSC |
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ADAS |
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Call Number |
ADAS @ adas @ DAS2010 |
Serial |
1424 |
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Author |
Diego Alejandro Cheda; Daniel Ponsa; Antonio Lopez |
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Title |
Camera Egomotion Estimation in the ADAS Context |
Type |
Conference Article |
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Year |
2010 |
Publication |
13th International IEEE Annual Conference on Intelligent Transportation Systems |
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Pages |
1415–1420 |
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Abstract |
Camera-based Advanced Driver Assistance Systems (ADAS) have concentrated many research efforts in the last decades. Proposals based on monocular cameras require the knowledge of the camera pose with respect to the environment, in order to reach an efficient and robust performance. A common assumption in such systems is considering the road as planar, and the camera pose with respect to it as approximately known. However, in real situations, the camera pose varies along time due to the vehicle movement, the road slope, and irregularities on the road surface. Thus, the changes in the camera position and orientation (i.e., the egomotion) are critical information that must be estimated at every frame to avoid poor performances. This work focuses on egomotion estimation from a monocular camera under the ADAS context. We review and compare egomotion methods with simulated and real ADAS-like sequences. Basing on the results of our experiments, we show which of the considered nonlinear and linear algorithms have the best performance in this domain. |
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Madeira Island (Portugal) |
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2153-0009 |
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978-1-4244-7657-2 |
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ITSC |
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ADAS |
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ADAS @ adas @ CPL2010 |
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1425 |
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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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Conference Article |
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Year |
2010 |
Publication |
IEEE Intelligent Vehicles Symposium |
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621–626 |
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Keywords |
road detection |
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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. |
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San Diego; CA; USA |
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IV |
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ADAS;ISE |
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no |
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Call Number |
ADAS @ adas @ ALG2010 |
Serial |
1428 |
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Author |
Jaume Amores; David Geronimo; Antonio Lopez |
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Title |
Multiple instance and active learning for weakly-supervised object-class segmentation |
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Conference Article |
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Year |
2010 |
Publication |
3rd IEEE International Conference on Machine Vision |
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Multiple Instance Learning; Active Learning; Object-class segmentation. |
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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. |
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Hong-Kong |
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ICMV |
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ADAS |
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ADAS @ adas @ AGL2010b |
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1429 |
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Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
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Conference Article |
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Year |
2011 |
Publication |
14th International Conference on Computer Analysis of Images and Patterns |
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6855 |
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II |
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463-470 |
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Keywords |
Pedestrian Detection; Color |
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Abstract |
Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution. |
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Seville, Spain |
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Springer |
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Berlin Heidelberg |
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P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch |
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English |
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english |
Original Title |
Color Contribution to Part-Based Person Detection in Different Types of Scenarios |
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0302-9743 |
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978-3-642-23677-8 |
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CAIP |
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ADAS @ adas @ RVL2011b |
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1665 |
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