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Author Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez edit  doi
openurl 
  Title Solving the Multi Object Occlusion Problem in a Multiple Camera Tracking System Type Journal
  Year 2009 Publication Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume 19 Issue 1 Pages (up) 165-171  
  Keywords  
  Abstract An efficient method to overcome adverse effects of occlusion upon object tracking is presented. The method is based on matching paths of objects in time and solves a complex occlusion-caused problem of merging separate segments of the same path.  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1054-6618 ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number ISE @ ise @ MAR2009a Serial 1160  
Permanent link to this record
 

 
Author Ivan Huerta; Ariel Amato; Xavier Roca; Jordi Gonzalez edit   pdf
doi  openurl
  Title Exploiting Multiple Cues in Motion Segmentation Based on Background Subtraction Type Journal Article
  Year 2013 Publication Neurocomputing Abbreviated Journal NEUCOM  
  Volume 100 Issue Pages (up) 183–196  
  Keywords Motion segmentation; Shadow suppression; Colour segmentation; Edge segmentation; Ghost detection; Background subtraction  
  Abstract This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motionsegmentation. In our first contribution, a case analysis of motionsegmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motionsegmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier 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  
  Notes ISE Approved no  
  Call Number Admin @ si @ HAR2013 Serial 1808  
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Author Albert Ali Salah; Theo Gevers; Nicu Sebe; Alessandro Vinciarelli edit  openurl
  Title Computer Vision for Ambient Intelligence Type Journal Article
  Year 2011 Publication Journal of Ambient Intelligence and Smart Environments Abbreviated Journal JAISE  
  Volume 3 Issue 3 Pages (up) 187-191  
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  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ SGS2011a Serial 1725  
Permanent link to this record
 

 
Author Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund edit  url
openurl 
  Title Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields Type Journal Article
  Year 2016 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 80 Issue Pages (up) 208–215  
  Keywords  
  Abstract This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset.
The approach shows that simple multi-modal features with the power of the MMSSL
paradigm can achieve better performance than state of the art results on the same dataset.
 
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA; ISE;MILAB; 600.098; 600.119 Approved no  
  Call Number Admin @ si @ TEG2016 Serial 2843  
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Author Maria Vanrell; Jordi Vitria; Xavier Roca edit  openurl
  Title A multidimensional scaling approach to explore the behavior of a texture perception algorithm. Type Journal Article
  Year 1997 Publication Machine Vision and Applications Abbreviated Journal  
  Volume 9 Issue Pages (up) 262–271  
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  Area Expedition Conference  
  Notes OR;ISE;CIC;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ VVR1997 Serial 35  
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