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Author Ferran Diego; Jose Manuel Alvarez; Joan Serrat; Antonio Lopez edit   pdf
doi  isbn
openurl 
  Title (up) 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 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 Joan Arnedo-Moreno; Agata Lapedriza edit  openurl
  Title (up) Visualizing key authenticity: turning your face into your public key Type Conference Article
  Year 2010 Publication 6th China International Conference on Information Security and Cryptology Abbreviated Journal  
  Volume Issue Pages 605-618  
  Keywords  
  Abstract Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference Inscrypt  
  Notes OR;MV Approved no  
  Call Number Admin @ si @ ArL2010c Serial 2149  
Permanent link to this record
 

 
Author Jaume Amores edit  doi
isbn  openurl
  Title (up) 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 ADAS @ adas @ Amo2010 Serial 1295  
Permanent link to this record
 

 
Author Santiago Segui; Laura Igual; Jordi Vitria edit  doi
isbn  openurl
  Title (up) Weighted Bagging for Graph based One-Class Classifiers Type Conference Article
  Year 2010 Publication 9th International Workshop on Multiple Classifier Systems Abbreviated Journal  
  Volume 5997 Issue Pages 1-10  
  Keywords  
  Abstract Most conventional learning algorithms require both positive and negative training data for achieving accurate classification results. However, the problem of learning classifiers from only positive data arises in many applications where negative data are too costly, difficult to obtain, or not available at all. Minimum Spanning Tree Class Descriptor (MSTCD) was presented as a method that achieves better accuracies than other one-class classifiers in high dimensional data. However, the presence of outliers in the target class severely harms the performance of this classifier. In this paper we propose two bagging strategies for MSTCD that reduce the influence of outliers in training data. We show the improved performance on both real and artificially contaminated data.  
  Address Cairo, Egypt  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-12126-5 Medium  
  Area Expedition Conference MCS  
  Notes MILAB;OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ SIV2010 Serial 1284  
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell edit  openurl
  Title (up) Who Painted this Painting? Type Conference Article
  Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal  
  Volume Issue Pages 329–333  
  Keywords  
  Abstract  
  Address Gjovik (Norway)  
  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 CREATE  
  Notes CIC Approved no  
  Call Number CAT @ cat @ KWV2010 Serial 1329  
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