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Author Saad Minhas; Zeba Khanam; Shoaib Ehsan; Klaus McDonald Maier; Aura Hernandez-Sabate edit  doi
openurl 
  Title Weather Classification by Utilizing Synthetic Data Type Journal Article
  Year 2022 Publication Sensors Abbreviated Journal SENS  
  Volume 22 Issue 9 Pages 3193  
  Keywords Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems  
  Abstract Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.  
  Address 21 April 2022  
  Corporate Author Thesis  
  Publisher MDPI 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; 600.139; 600.159; 600.166; 600.145; Approved no  
  Call Number Admin @ si @ MKE2022 Serial 3761  
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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 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 (up) 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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Author Aura Hernandez-Sabate; Debora Gil edit   pdf
url  doi
isbn  openurl
  Title The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries Type Book Chapter
  Year 2012 Publication Intravascular Ultrasound Abbreviated Journal  
  Volume Issue Pages 185-206  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Intech Place of Publication Editor Yasuhiro Honda  
  Language English Summary Language (up) english Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-953-307-900-4 Medium  
  Area Expedition Conference  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ HeG2012 Serial 1684  
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