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Author Patricia Marquez; Debora Gil; R.Mester; Aura Hernandez-Sabate edit   pdf
openurl 
  Title Local Analysis of Confidence Measures for Optical Flow Quality Evaluation Type Conference Article
  Year 2014 Publication 9th International Conference on Computer Vision Theory and Applications Abbreviated Journal  
  Volume 3 Issue Pages (up) 450-457  
  Keywords Optical Flow; Confidence Measure; Performance Evaluation.  
  Abstract Optical Flow (OF) techniques facing the complexity of real sequences have been developed in the last years. Even using the most appropriate technique for our specific problem, at some points the output flow might fail to achieve the minimum error required for the system. Confidence measures computed from either input data or OF output should discard those points where OF is not accurate enough for its further use. It follows that evaluating the capabilities of a confidence measure for bounding OF error is as important as the definition
itself. In this paper we analyze different confidence measures and point out their advantages and limitations for their use in real world settings. We also explore the agreement with current tools for their evaluation of confidence measures performance.		  
  Address Lisboa; January 2014  
  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 VISAPP  
  Notes IAM; ADAS; 600.044; 600.060; 600.057; 601.145; 600.076; 600.075 Approved no  
  Call Number Admin @ si @ MGM2014 Serial 2432  
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Author Patricia Marquez; Debora Gil; Aura Hernandez-Sabate edit   pdf
url  doi
openurl 
  Title Evaluation of the Capabilities of Confidence Measures for Assessing Optical Flow Quality Type Conference Article
  Year 2013 Publication ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars Abbreviated Journal  
  Volume Issue Pages (up) 624-631  
  Keywords  
  Abstract Assessing Optical Flow (OF) quality is essential for its further use in reliable decision support systems. The absence of ground truth in such situations leads to the computation of OF Confidence Measures (CM) obtained from either input or output data. A fair comparison across the capabilities of the different CM for bounding OF error is required in order to choose the best OF-CM pair for discarding points where OF computation is not reliable. This paper presents a statistical probabilistic framework for assessing the quality of a given CM. Our quality measure is given in terms of the percentage of pixels whose OF error bound can not be determined by CM values. We also provide statistical tools for the computation of CM values that ensures a given accuracy of the flow field.  
  Address Sydney; Australia; December 2013  
  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 CVTT:E2M  
  Notes IAM; ADAS; 600.044; 600.057; 601.145 Approved no  
  Call Number Admin @ si @ MGH2013b Serial 2351  
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Author Patricia Marquez; Debora Gil; Aura Hernandez-Sabate edit   pdf
url  doi
openurl 
  Title A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth Type Conference Article
  Year 2011 Publication IEEE International Conference on Computer Vision – Workshops Abbreviated Journal  
  Volume Issue Pages (up) 2042-2049  
  Keywords IEEE International Conference on Computer Vision – Workshops  
  Abstract Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems.  
  Address  
  Corporate Author Thesis  
  Publisher IEEE Place of Publication Barcelona (Spain) Editor  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICCVW  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ MGH2011 Serial 1682  
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