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Hand posture recognition using mathematical morphology

Autorzy
Identyfikatory
Warianty tytułu
PL
Wykorzystanie morfologii matematycznej do rozpoznawania kształtu dłoni
Języki publikacji
EN
Abstrakty
EN
The paper presents a new method of hand posture recognition. To construct the feature vector the method uses results of morphological hit or miss transforming elements representing characteristic fragments of the hand. Details of the solution are described. Results of the Polish Finger Alphabet (PFA) recognition with neural networks and their fusions are given. The hand postures encountered in PFA are complicated enough, so the proposed method can be useful for recognizing other gestures. We show it for eight signs that can be used in communications with a robot.
PL
W pracy została przedstawiona nowa metoda rozpoznawania kształtu dłoni. Wykorzystuje ona morfologiczną transformację trafi-nie-trafi obrazu binarnego. Szczegółowo omówiono zagadnienie konstruowania wektora cech. Przedstawiono wyniki rozpoznawania znaków Polskiego Alfabetu Palcowego i kilku innych gestów. Do klasyfikacji stosowano sieci neuronowe.
Słowa kluczowe
Rocznik
Strony
279--293
Opis fizyczny
Bibliogr. poz. 22, rys.
Twórcy
autor
  • Rzeszów University of Technology, Computer and Control Engineering Chair, W. Pola 2, 35-959 Rzeszów, Poland
autor
  • Rzeszów University of Technology, Computer and Control Engineering Chair, W. Pola 2, 35-959 Rzeszów, Poland
Bibliografia
  • [1] S. B.Cho, J. H. Kim, Multiple Network Fusion Using Fuzzy Logic. IEEE Trans, on Neural Networks 6, 2, 1995, 497-501.
  • [2] R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, J. Willey & Sons, Inc., New York, 2001.
  • [3]W. T. Freeman, M. Roth, Orientation histograms for hand gesture recognition, Proc. of the IEEE Intnl. Workshop on Automatic Face and Gesture Recogni¬tion, Zurich, 1995, 296-301.
  • [4]S. Gong, S. J. McKenna, A. Psarrou, Dynamic Vision: from Images to Face Recognition, Imperial Colege Press, London, 2000.
  • [5]J. K. Hendzel, Dictionary of the Polish Sign Language, Wyd. OFFER, Olsztyn 1995 (in Polish).
  • [6]E. Hunter, J. Schlenzig, R. Jain, Posture estimation in reduced model gesture input system, Proc. of the IEEE Intnl. Workshop on Automatic Face and Gesture Recognition, Zurich, 1995, 290-295.
  • [7]M. Lades, J. C. Vorbriiggen, J. Buchman, J. Lange, C. von der Malsburg, R. P. Wiirtz, W. Konen, Distortion invariant object recognition in the dynamic link architecture, IEEE Trans. On Computers, 42(3), 1993, 300-311.
  • [8]J. Marnik: Recognition of the Polish linger Alphabet Using Mathematical Morphology and Neural Networks, PhD Thesis, Academy of Mining and Metallurgy, Kraków, 2002 (in Polish).
  • [9]J. Martin, J. L. Crowley, An appearance-based approach to gesture recognition, Proc of the 9th Intnl. Conf on Image Analysis and Processing, Florence, Italy, 1997.
  • [10]C. Nolker, H. Ritter, Detection of fingertips in human hand movement sequences, Proc. of the Intnl. Gesture Workshop, Bielefeld, 1997, 209-218.
  • [11]V. I. Pavlovic, R. Sharma, T. S. Huang: Visual Interpretation of Hand Gestures for Human-Computer Interaction, IEEE Trans. PAMI, 19, 7, 1997, 677-695.
  • [12]N. Petkov, Biologically motivated image classification system, [in:] Laplante P. A., Stoyenko A. D., (eds.), Real-Time Imaging, IEEE Press, New York, 1996, 195-223.
  • [13]J. M. Rehg, T. Kanadę, Visual tracking of high DOF articulated structures on application to human hand tracking, [in:] Ecklundh J. O., (ed.), Lectures and Notes in Computer Science, vol. 80, Springer, New York, 1994, 34-46.
  • [14]M. Sonka, V. Hlavac, R. Boyle, Image Processing, Analysis and Machine Vision, Chapman & Hall, London, 1994.
  • [15]StatSoft Inc.: Statistica Neural Networks, 1998.
  • [16]R. Tadeusiewicz, R Korohoda, Computer Image Analysis and Processing, Wyd. Fund. Post. Telekom., Krakw, 1997 (in Polish).
  • [17]J-Ch. Terrillon, M. N. Shirazi, H. Fukamachi, S. Akamatsu, Comparative Performance Models and Crominance Spaces for the Automatic Detection of Human Faces in Color Images, Proc. of the Fourth Intnl. Conf. on Automatic Face and Gesture Recognition, Grenoble, France 2000, 54-61.
  • [18]J. Triesch, C. von der Malsburg, Robust classification of hand postures against complex background, Proc of the 2nd Intnl. Conf. on Automatic Face and Gesture Recognition, Killington, Vermont, 1996, 170-175.
  • [19]J. Triesch, C. von der Malsburg, A gesture interface for human-robot interaction, Proc of the 2nd Intnl. Conf. on Automatic Face and Gesture Recognition, Nara, Japan, 1998, 546-551.
  • [20]J. Triesch, C. von der Malsburg, A system for person independent hand posture recognition against complex backgrounds, IEEE Trans, on Pattern Recognition and Machine Intelligence, 23, 12, 2001, 1449-1453.
  • [21]M. H. Yang, N. Ahuya, Face detection and gesture recognition for human- computer interaction, Cluver Acad. Publ., Boston, 2001.
  • [22]M. Zhao, F. K. H. Quek, X. Wu, RIEVL: Recursive induction learning in hand gesture recognition, IEEE Trans, on Pattern Recognition and Machine Intelligence, 20(11), 1998, 1174-1185.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ3-0002-0035
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