@InProceedings{TeodoroBernDigi:2017:SkCoSe,
               author = "Teodoro, Beatriz Tomazela and Bernardes, Jo{\~a}o and 
                         Digiampietri, Luciano Antonio",
          affiliation = "USP and USP and USP",
                title = "Skin Color Segmentation and Leveshtein Distance Recognition of BSL 
                         Signs in Video",
            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 = "sign language recognition, image processing, human skin 
                         segmentation, Brazilian Sign Language, LIBRAS.",
             abstract = "Sign language automatic recognition is an important research area 
                         with open challenges that aims to mitigate the obstacles in the 
                         daily lives of people who are deaf or hard of hearing and increase 
                         their integration in the predominantly hearing society in which we 
                         live. This paper implements, evaluates and discusses strategies 
                         for automatic recognition of Brazilian Sign Language (BSL) signs, 
                         which ultimately aims to simplify the communication between deaf 
                         signing in BSL and listeners who do not know this sign language, 
                         accomplished through the processing of digital videos of people 
                         communicating in BSL without the use of colored gloves or data 
                         gloves and sensors or the requirement of high quality recordings 
                         in laboratories with controlled backgrounds or lighting. An 
                         approach divided in several stages was developed and all stages of 
                         the proposed system can be considered contributions for future 
                         works in sign language recognition or those involving image 
                         processing, human skin segmentation, object tracking etc. For the 
                         skin color based segmentation stage, in particular, several 
                         techniques were implemented and compared and the strategy used for 
                         sign recognition, exploring the Leveshtein distance and a voting 
                         scheme with a binary classifier, is unusual in this area and 
                         showed good results. From the original 600 samples of 30 words, 
                         chosen for frequency of use and superposition of sign elements to 
                         make recognition more complex, the system was able to correctly 
                         segment 422 (70%) signs, for which it reached 100% accuracy in 
                         recognition using our strategy. This sign database with 600 
                         samples in video of the chosen 30 word vocabulary is another of 
                         this works contributions and is available upon request to the 
                         authors.",
  conference-location = "Niter{\'o}i, RJ, Brazil",
      conference-year = "17-20 Oct. 2017",
                  doi = "10.1109/SIBGRAPI.2017.19",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.19",
             language = "en",
                  ibi = "8JMKD3MGPAW/3PF84FS",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3PF84FS",
           targetfile = "Skin_Color_Segmentation_and_Leveshtein_Distance.pdf",
        urlaccessdate = "2024, May 02"
}