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Author Joana Maria Pujadas-Mora; Alicia Fornes; Oriol Ramos Terrades; Josep Llados; Jialuo Chen; Miquel Valls-Figols; Anna Cabre edit  doi
openurl 
  Title The Barcelona Historical Marriage Database and the Baix Llobregat Demographic Database. From Algorithms for Handwriting Recognition to Individual-Level Demographic and Socioeconomic Data Type (up) Journal
  Year 2022 Publication Historical Life Course Studies Abbreviated Journal HLCS  
  Volume 12 Issue Pages 99-132  
  Keywords Individual demographic databases; Computer vision, Record linkage; Social mobility; Inequality; Migration; Word spotting; Handwriting recognition; Local censuses; Marriage Licences  
  Abstract The Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451–1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography.  
  Address June 23, 2022  
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  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ PFR2022 Serial 3737  
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Author Gemma Sanchez; Josep Llados; K. Tombre edit  doi
openurl 
  Title A mean string algorithm to compute the average among a set of 2D shapes Type (up) Journal Article
  Year 2002 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 23 Issue 1-3 Pages 203–214  
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  Notes DAG; IF: 0.409 Approved no  
  Call Number DAG @ dag @ SLT2002 Serial 275  
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Author Antonio Lopez; Ernest Valveny; Juan J. Villanueva edit  url
openurl 
  Title Real-time quality control of surgical material packaging by artificial vision Type (up) Journal Article
  Year 2005 Publication Assembly Automation Abbreviated Journal  
  Volume 25 Issue 3 Pages  
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  Abstract IF: 0.061)  
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  Notes ADAS;DAG Approved no  
  Call Number ADAS @ adas @ LVV2005 Serial 552  
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Author Oriol Ramos Terrades; Ernest Valveny edit  doi
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  Title A new use of the ridgelets transform for describing linear singularities in images Type (up) Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 27 Issue 6 Pages 587–596  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ RaV2006a Serial 635  
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Author Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik edit   pdf
doi  openurl
  Title Asymmetric Distances for Binary Embeddings Type (up) Journal Article
  Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 36 Issue 1 Pages 33-47  
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  Abstract In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques.  
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  ISSN 0162-8828 ISBN Medium  
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  Notes DAG; 600.045; 605.203; 600.077 Approved no  
  Call Number Admin @ si @ GPG2014 Serial 2272  
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