?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.title=Separating+a+Real-Life+Nonlinear+Image+Mixture&rft.creator=Almeida%2C+Luis+B.&rft.subject=Statistical+Models&rft.subject=Machine+Learning&rft.subject=Neural+Nets&rft.subject=Artificial+Intelligence&rft.description=When+acquiring+an+image+of+a+paper+document%2C+the+image+printed+on+the+back+page+sometimes+shows+through.+The+mixture+of+the+front-+and+back-page+images+thus+obtained+is+markedly+nonlinear%2C+and+thus+constitutes+a+good+real-life+test+case+for+nonlinear+blind+source+separation.%0A%0AThis+paper+addresses+a+difficult+version+of+this+problem%2C+corresponding+to+the+use+of+%22onion+skin%22+paper%2C+which+results+in+a+relatively+strong+nonlinearity+of+the+mixture%2C+which+becomes+close+to+singular+in+the+lighter+regions+of+the+images.+The+separation+is+achieved+through+the+MISEP+technique%2C+which+is+an+extension+of+the+well+known+INFOMAX+method.+The+separation+results+are+assessed+with+objective+quality+measures.+They+show+an+improvement+over+the+results+obtained+with+linear+separation%2C+but+have+room+for+further+improvement.&rft.date=2005-05&rft.type=Preprint&rft.type=NonPeerReviewed&rft.format=application%2Fpdf&rft.identifier=http%3A%2F%2Fcogprints.org%2F4360%2F1%2FAlmeidaJMLR05.pdf&rft.format=application%2Fpostscript&rft.identifier=http%3A%2F%2Fcogprints.org%2F4360%2F2%2FAlmeidaJMLR05.ps&rft.identifier=++Almeida%2C+Luis+B.++(2005)+Separating+a+Real-Life+Nonlinear+Image+Mixture.++%5BPreprint%5D+++++&rft.relation=http%3A%2F%2Fcogprints.org%2F4360%2F