%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 01:08:50 PM

@INPROCEEDINGS{Moreira-96,
         author = {Moreira, Miguel and Fiesler, Emile and Pante, Gianni},
         editor = {Soto, R. and Sanchez, J. M. and Campbell, M. and Cantu, F. J.},
       projects = {Idiap},
          title = {Image Classification by Neural Networks for the Quality Control of Watches},
      booktitle = {Proceedings ISAI /IFIS 1996},
        chapter = {Manufactur},
           year = {1996},
      publisher = {ITESM},
       location = {Cancun, Mexico},
   organization = {ITESM},
        address = {Monterey, Mexico},
           isbn = {968-29-9437-3},
       crossref = {mfp-96},
       abstract = {A method is presented for the automatic time detection of watches, where the hands are classified by a neural network. In order to reduce the overall cost of data collection, strict limits were imposed on the data collection time. This constraint severely limits the available amount of images, and poses the challenge to solve the hand recognition problem with a minimum amount of training and test data. Two neural network approaches are presented together with their performance results, which show an excellent final recognition rate.},
dates={12--15 November 1996},
ieeecn={96TH8235},
ipdmembership={learning},
language={English},
}



crossreferenced publications: 
@TECHREPORT{MFP-96,
         author = {Moreira, Miguel and Fiesler, Emile and Pante, Gianni},
       projects = {Idiap},
          title = {Image Classification by Neural Networks for the Quality Control of Watches},
           type = {Idiap-RR},
         number = {Idiap-RR-10-1996},
           year = {1996},
    institution = {IDIAP},
       abstract = {A method is presented for the automatic time detection of watches, where the hands are classified by a neural network. In order to reduce the overall cost of data collection, strict limits were imposed on the data collection time. This constraint severely limits the available amount of images, and poses the challenge to solve the hand recognition problem with a minimum amount of training and test data. Two neural network approaches are presented together with their performance results, which show an excellent final recognition rate.},
            pdf = {https://publications.idiap.ch/attachments/reports/1996/rr96-10.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/1996/rr96-10.ps.gz},
ipdmembership={learning},
}