author = "Oliveira, Thomas Jose Mazon de and {Marco Aurelio Wehrmeister} and 
                         Nassu, Bogdan Tomoyuki",
          affiliation = "{Federal University of Technology - Parana} and {Federal 
                         University of Technology - Parana} and {Federal University of 
                         Technology - Parana}",
                title = "Detecting Modifications in Printed Circuit Boardsfrom Fuel Pump 
            booktitle = "Proceedings...",
                 year = "2017",
               editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and 
                         Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and 
                         Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba, 
                         Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo 
                         and Vital, Creto and Pagot, Christian Azambuja and Petronetto, 
                         Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
         organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Image-based detection of modifications in printed circuit boards, 
                         Fraud detection in fuel pumps, Computer vision, image 
                         registration, machine learning, Points of interest.",
             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.",
  conference-location = "Niter{\'o}i, RJ",
      conference-year = "Oct. 17-20, 2017",
             language = "en",
           targetfile = "SibGrapi.pdf",
        urlaccessdate = "2021, Jan. 25"