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Author David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo edit  url
isbn  openurl
  Title Real-time Object Segmentation using a Bag of Features Approach Type Conference Article
  Year 2010 Publication 13th International Conference of the Catalan Association for Artificial Intelligence Abbreviated Journal  
  Volume 220 Issue Pages 321–329  
  Keywords Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors  
  Abstract 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.  
  Address  
  Corporate Author Thesis  
  Publisher IOS Press Amsterdam, Place of Publication Editor In R.Alquezar, A.Moreno, J.Aguilar.  
  Language Summary Language (up) Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 9781607506423 Medium  
  Area Expedition Conference CCIA  
  Notes ADAS Approved no  
  Call Number Admin @ si @ ARL2010b Serial 1417  
Permanent link to this record
 

 
Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa edit   pdf
openurl 
  Title Multiple-target tracking for the intelligent headlights control Type Conference Article
  Year 2010 Publication 13th Annual International Conference on Intelligent Transportation Systems Abbreviated Journal  
  Volume Issue Pages 903–910  
  Keywords Intelligent Headlights  
  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.
 
  Address Madeira Island (Portugal)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language (up) 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 ADAS @ adas @ RSL2010 Serial 1422  
Permanent link to this record
 

 
Author Ferran Diego; Daniel Ponsa; Joan Serrat; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Vehicle geolocalization based on video synchronization Type Conference Article
  Year 2010 Publication 13th Annual International Conference on Intelligent Transportation Systems Abbreviated Journal  
  Volume Issue Pages 1511–1516  
  Keywords video alignment  
  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.
 
  Address Madeira Island (Portugal)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language (up) Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2153-0009 ISBN 978-1-4244-7657-2 Medium  
  Area Expedition Conference ITSC  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DPS2010 Serial 1423  
Permanent link to this record
 

 
Author Ferran Diego; Jose Manuel Alvarez; Joan Serrat; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Vision-based road detection via on-line video registration Type Conference Article
  Year 2010 Publication 13th Annual International Conference on Intelligent Transportation Systems Abbreviated Journal  
  Volume Issue Pages 1135–1140  
  Keywords video alignment; road detection  
  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.
 
  Address Madeira Island (Portugal)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language (up) Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2153-0009 ISBN 978-1-4244-7657-2 Medium  
  Area Expedition Conference ITSC  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ DAS2010 Serial 1424  
Permanent link to this record
 

 
Author Diego Alejandro Cheda; Daniel Ponsa; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title Camera Egomotion Estimation in the ADAS Context Type Conference Article
  Year 2010 Publication 13th International IEEE Annual Conference on Intelligent Transportation Systems Abbreviated Journal  
  Volume Issue Pages 1415–1420  
  Keywords  
  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.  
  Address Madeira Island (Portugal)  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language (up) Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2153-0009 ISBN 978-1-4244-7657-2 Medium  
  Area Expedition Conference ITSC  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ CPL2010 Serial 1425  
Permanent link to this record
 

 
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 (up) 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 ADAS @ adas @ ALG2010 Serial 1428  
Permanent link to this record
 

 
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 (up) 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 ADAS @ adas @ AGL2010b Serial 1429  
Permanent link to this record
 

 
Author Cristina Cañero; Petia Radeva; Oriol Pujol; Ricardo Toledo; Debora Gil; J. Saludes; Juan J. Villanueva; B. Garcia del Blanco; J. Mauri; E. Fernandez-Nofrerias; J.A. Gomez-Hospital; E. Iraculis; J. Comin; C. Quiles; F. Jara; A. Cequier; E. Esplugas edit   pdf
openurl 
  Title Optimal Stent Implantation: Three-dimensional Evaluation of the Mutual Position of Stent and Vessel via Intracoronary Ecography Type Conference Article
  Year 1999 Publication Proceedings of International Conference on Computer in Cardiology (CIC´99) Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract We present a new automatic technique to visualize and quantify the mutual position between the stent and the vessel wall by considering their three-dimensional reconstruction. Two deformable generalized cylinders adapt to the image features in all IVUS planes corresponding to the vessel wall and the stent in order to reconstruct the boundaries of the stent and the vessel in space. The image features that characterize the stent and the vessel wall are determined in terms of edge and ridge image detectors taking into account the gray level of the image pixels. We show that the 30 reconstruction by deformable cylinders is accurate and robust due to the spatial data coherence in the considered volumetric IVUS image. The main clinic utility of the stent and vessel reconstruction by deformable’ cylinders consists of its possibility to visualize and to assess the optimal stent introduction.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language (up) Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes MILAB; RV; IAM; ADAS; HuPBA Approved no  
  Call Number IAM @ iam @ CRP1999a Serial 1491  
Permanent link to this record
 

 
Author Cristina Cañero; Petia Radeva; Oriol Pujol; Ricardo Toledo; Debora Gil; J. Saludes; Juan J. Villanueva; B. Garcia del Blanco; Josefina Mauri; Eduard Fernandez-Nofrerias; J.A. Gomez-Hospital; E. Iraculis; J. Comin; C. Quiles; F. Jara; A. Cequier; E.Esplugas edit   pdf
openurl 
  Title Three-dimensional reconstruction and quantification of the coronary tree using intravascular ultrasound images Type Conference Article
  Year 1999 Publication Proceedings of International Conference on Computer in Cardiology (CIC´99) Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract In this paper we propose a new Computer Vision technique to reconstruct the vascular wall in space using a deformable model-based technique and compounding methods, based in biplane angiography and intravascular ultrasound data jicsion. It is also proposed a generalpurpose three-dimensional guided interpolation method. The three dimensional centerline of the vessel is reconstructed from geometrically corrected biplane angiographies using automatic segmentation methods and snakes. The IVUS image planes are located in the threedimensional space and correctly oriented. A led interpolation method based in B-SurJaces and snakes isused to fill the gaps among image planes  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language (up) Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CINC99  
  Notes MILAB;RV;IAM;ADAS;HuPBA Approved no  
  Call Number IAM @ iam @ CRP1999b Serial 1492  
Permanent link to this record
 

 
Author M.Gomez; Josefina Mauri; Eduard Fernandez-Nofrerias; Oriol Rodriguez-Leon; Carme Julia; Debora Gil; Petia Radeva edit  openurl
  Title Reconstrucción de un modelo espacio-temporal de la luz del vaso a partir de secuencias de ecografía intracoronaria Type Conference Article
  Year 2002 Publication XXXVIII Congreso Nacional de la Sociedad Española de Cardiología. Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language (up) Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes IAM;ADAS;MILAB Approved no  
  Call Number IAM @ iam @ GMF2002d Serial 1516  
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