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		<identifier>8JMKD3MGPAW/3PF6BJ8</identifier>
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		<citationkey>OliveiraNass:2017:DeMoPr</citationkey>
		<title>Detecting Modifications in Printed Circuit Boardsfrom Fuel Pump Controllers</title>
		<format>On-line</format>
		<year>2017</year>
		<numberoffiles>1</numberoffiles>
		<size>4683 KiB</size>
		<author>Oliveira, Thomas Jose Mazon de,</author>
		<author>Marco Aurelio Wehrmeister,</author>
		<author>Nassu, Bogdan Tomoyuki,</author>
		<affiliation>Federal University of Technology - Parana</affiliation>
		<affiliation>Federal University of Technology - Parana</affiliation>
		<affiliation>Federal University of Technology - Parana</affiliation>
		<editor>Torchelsen, Rafael Piccin,</editor>
		<editor>Nascimento, Erickson Rangel do,</editor>
		<editor>Panozzo, Daniele,</editor>
		<editor>Liu, Zicheng,</editor>
		<editor>Farias, Mylène,</editor>
		<editor>Viera, Thales,</editor>
		<editor>Sacht, Leonardo,</editor>
		<editor>Ferreira, Nivan,</editor>
		<editor>Comba, João Luiz Dihl,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Schiavon Porto, Marcelo,</editor>
		<editor>Vital, Creto,</editor>
		<editor>Pagot, Christian Azambuja,</editor>
		<editor>Petronetto, Fabiano,</editor>
		<editor>Clua, Esteban,</editor>
		<editor>Cardeal, Flávio,</editor>
		<e-mailaddress>thomasoliveira@alunos.utfpr.edu.br</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)</conferencename>
		<conferencelocation>Niterói, RJ</conferencelocation>
		<date>Oct. 17-20, 2017</date>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<tertiarytype>Full Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<keywords>Image-based detection of modifications in printed circuit boards, Fraud detection in fuel pumps, Computer vision, image registration, machine learning, Points of interest.</keywords>
		<abstract>Frauds involving illegal modifications to the printedcircuit boards from fuel pump controllers are a serious problem,which  not  only  harms  customers,  but  also  connects  to  othercrimes,  such  as  money  laundering  and  tax  evasion.  The  currentstate-of-practice  for  inspecting  these  boards  is  a  visual  analysisperformed  by  a  human.  In  this  paper,  we  introduce  an  image-based approach that can provide support to the human inspectorby   automatically   detecting   suspicious   regions   in   the   boards.The  proposed  approach  aligns  a  photograph  of  the  inspectedboard  to  a  reference  view,  partitions  the  image  in  sub-regions,extracts features using a variation of the popular Scale-InvariantFeature   Transform,   classifies   the   features   against   previouslytrained  Support  Vector  Machines,  and  integrates  the  results  forpresentation.  In  experiments  performed  on  a  dataset  containing649  images  from  a  board,  with  and  without  modifications,  ourapproach  achieved a  precision of  0.7739,  a recall  of 0.9434,  andanF-measure  0.8503.  These  results  indicate  that  our  approachcan  effectively  identify  suspicious  regions,  providing  invaluablehelp  to  the  human  inspector.</abstract>
		<language>en</language>
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		<usergroup>thomasoliveira@alunos.utfpr.edu.br</usergroup>
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		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2017/08.17.13.40</url>
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