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Author Nataliya Shapovalova edit  openurl
  Title On Importance of Interaction and Context Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 155 Issue Pages  
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  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ Sha2010 Serial (down) 1355  
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Author Jon Almazan edit  openurl
  Title Deforming the Blurred Shape Model for Shape Description and Recognition Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 163 Issue Pages  
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  Corporate Author Thesis Master's thesis  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number Admin @ si @ Alm2010 Serial (down) 1354  
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Author David Fernandez edit  openurl
  Title Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 161 Issue Pages  
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  Corporate Author Thesis Master's thesis  
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  Notes DAG Approved no  
  Call Number Admin @ si @ Fer2010b Serial (down) 1353  
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Author Ekain Artola edit  openurl
  Title Human Attention Map Prediction Combining Visual Features Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 160 Issue Pages  
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  Corporate Author Thesis Bachelor's thesis  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number Admin @ si @ Art2010 Serial (down) 1352  
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Author Anjan Dutta edit  openurl
  Title Symbol Spotting in Graphical Documents by Serialized Subgraph Matching Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 159 Issue Pages  
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  Corporate Author Thesis Master's thesis  
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  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ Dut2010 Serial (down) 1351  
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Author Lluis Pere de las Heras edit  openurl
  Title Syntactic Model for Semantic Document Analysis Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 158 Issue Pages  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number Admin @ si @ Per2010 Serial (down) 1350  
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Author Jorge Bernal; Fernando Vilariño; F. Javier Sanchez edit   pdf
openurl 
  Title Feature Detectors and Feature Descriptors: Where We Are Now Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 154 Issue Pages  
  Keywords  
  Abstract Feature Detection and Feature Description are clearly nowadays topics. Many Computer Vision applications rely on the use of several of these techniques in order to extract the most significant aspects of an image so they can help in some tasks such as image retrieval, image registration, object recognition, object categorization and texture classification, among others. In this paper we define what Feature Detection and Description are and then we present an extensive collection of several methods in order to show the different techniques that are being used right now. The aim of this report is to provide a glimpse of what is being used currently in these fields and to serve as a starting point for future endeavours.  
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  ISSN ISBN Medium  
  Area 800 Expedition Conference  
  Notes MV;SIAI Approved no  
  Call Number Admin @ si @ BVS2010; IAM @ iam @ BVS2010 Serial (down) 1348  
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Author Antonio Hernandez edit  openurl
  Title Pose and Face Recovery via Spatio-temporal GrabCut Human Segmentation Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 153 Issue Pages  
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  Corporate Author Thesis Master's thesis  
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  Area Expedition Conference  
  Notes HUPBA;MILAB Approved no  
  Call Number Admin @ si @ Her2010 Serial (down) 1347  
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Author Ahmed Mounir Gad edit  openurl
  Title Object Localization Enhancement by Multiple Segmentation Fusion Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 152 Issue Pages  
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  Corporate Author Thesis Master's thesis  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number Admin @ si @ Mou2010 Serial (down) 1346  
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Author Marc Serra edit  openurl
  Title Estimating Intrinsic Images from Physical and Categorical Color Cues Type Report
  Year 2010 Publication CVC Technical Report Abbreviated Journal  
  Volume 151 Issue Pages  
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  Corporate Author Thesis Master's thesis  
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  Notes CIC Approved no  
  Call Number Admin @ si @ Ser2010 Serial (down) 1345  
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Author Naveen Onkarappa; Angel Sappa edit  doi
isbn  openurl
  Title On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow Type Conference Article
  Year 2010 Publication 7th International Conference on Image Analysis and Recognition Abbreviated Journal  
  Volume 6111 Issue Pages 230-239  
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  Abstract This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach.  
  Address Povoa de Varzim (Portugal)  
  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 978-3-642-13771-6 Medium  
  Area Expedition Conference ICIAR  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ OnS2010 Serial (down) 1342  
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Author David Geronimo; Angel Sappa; Daniel Ponsa; Antonio Lopez edit   pdf
url  doi
openurl 
  Title 2D-3D based on-board pedestrian detection system Type Journal Article
  Year 2010 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 114 Issue 5 Pages 583–595  
  Keywords Pedestrian detection; Advanced Driver Assistance Systems; Horizon line; Haar wavelets; Edge orientation histograms  
  Abstract During the next decade, on-board pedestrian detection systems will play a key role in the challenge of increasing traffic safety. The main target of these systems, to detect pedestrians in urban scenarios, implies overcoming difficulties like processing outdoor scenes from a mobile platform and searching for aspect-changing objects in cluttered environments. This makes such systems combine techniques in the state-of-the-art Computer Vision. In this paper we present a three module system based on both 2D and 3D cues. The first module uses 3D information to estimate the road plane parameters and thus select a coherent set of regions of interest (ROIs) to be further analyzed. The second module uses Real AdaBoost and a combined set of Haar wavelets and edge orientation histograms to classify the incoming ROIs as pedestrian or non-pedestrian. The final module loops again with the 3D cue in order to verify the classified ROIs and with the 2D in order to refine the final results. According to the results, the integration of the proposed techniques gives rise to a promising system.  
  Address Computer Vision and Image Understanding (Special Issue on Intelligent Vision Systems), Vol. 114(5):583-595  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1077-3142 ISBN Medium  
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  Notes ADAS Approved no  
  Call Number ADAS @ adas @ GSP2010 Serial (down) 1341  
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Author David Geronimo; Antonio Lopez; Angel Sappa; Thorsten Graf edit   pdf
url  doi
openurl 
  Title Survey on Pedestrian Detection for Advanced Driver Assistance Systems Type Journal Article
  Year 2010 Publication IEEE Transaction on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 32 Issue 7 Pages 1239–1258  
  Keywords ADAS, pedestrian detection, on-board vision, survey  
  Abstract Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ GLS2010 Serial (down) 1340  
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Author Sergio Escalera; Oriol Pujol; Petia Radeva edit  url
doi  openurl
  Title Re-coding ECOCs without retraining Type Journal Article
  Year 2010 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 31 Issue 7 Pages 555–562  
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  Abstract A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations.  
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  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
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  Notes MILAB;HUPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2010e Serial (down) 1338  
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Author Umapada Pal; Partha Pratim Roy; N. Tripathya; Josep Llados edit  url
doi  openurl
  Title Multi-oriented Bangla and Devnagari text recognition Type Journal Article
  Year 2010 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 43 Issue 12 Pages 4124–4136  
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  Abstract There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters.

For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters.		  
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  Publisher Elsevier Place of Publication Editor  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ PRT2010 Serial (down) 1337  
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