PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Representing the finger-only topology for hand shape recognition

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Automatic recognition of hand shapes in a moving image sequence requires the elements of hand tracking, feature extraction and classification. We have developed a robust tracking algorithm and a new hand shape representation technique that characterises the finger-only topology of the hand by adapting an existing technique from speech signal processing. The tracking algorithm determines the centre of the largest convex subset of the hand throughout an image sequence, using a combination of pattern matching and condensation algorithms. A hand shape feature represents the topological formation of the finger-only regions of the hand using a Linear Predictive Coding parameter set called cepstral coefficients. Feature extraction is performed on the polar dimensions of the hand region-of-interest, by tracking the finger-only region and extracting euclidean distances between the finger-only contour and the hand centre, which are then converted into cepstral coefficients. Experiments are conducted using mug-grabbing sequences to recognise four different hand shapes. Results demonstrate the robustness of hand tracking on cluttered backgrounds and the effectiveness of the hand shape representation technique on varying hand shapes.
Rocznik
Strony
187--202
Opis fizyczny
Bibliogr. 13 poz., fot., rys.
Twórcy
autor
  • Scool of Computer Science & Software Engineering, The Uniwersity of Western Australia, 35 Stirling Highway, Crawley, W.A. 6009, Australia
autor
  • Scool of Computer Science & Software Engineering, The Uniwersity of Western Australia, 35 Stirling Highway, Crawley, W.A. 6009, Australia
Bibliografia
  • [1] Durbin, J.: The fitting of time-series models. Rev. Inst. Int. de Stat., 28(3), 233-244. 1960.
  • [2] Ohta Y.,Kanade T., Sakai T.: Color information for region segmentation. CGIP, 13, 222-241. 1980.
  • [3] Manly B.: Multivariate Statistical Methods: A Primer. Chapman and Hall. 1986.
  • [4] Rabiner L., Juang B.W.: Fundamentals of Speech Recognition. Prentice Hall. 1993.
  • [5] Uras C., Verri A.: Hand gesture recognition from edge maps. Proc. Int. Workshop on Automatic Face and Gesture Recognition, 116-121. 1995.
  • [6] Black M., Jepson A.: Eigentracking: robust matching and tracking of articulated objects using a view-based representation. Proc. 4th European Conf. on Computer Vision, 329-342. 1996.
  • [7] Pavlovic V.L., Sbarma R., Huang T.: Visual interpretation of band gestures for human-computer interaction: A review. IEEE Trans. on PAMI, 19(7), 677-695. 1997.
  • [8] Blake A., Isard M.: Active Contours. Springer. 1998.
  • [9] Kurita T., Hayamizu S.: Gesture recognition using HLAC features of PARCOR images and hmm based recognizer. Proc. Int. Workshop on Automatic Face and Gesture Recognition, 422-433. 1998.
  • [10] Shimada N., Shirai Y., Kuno Y., Miura J.: Hand gesture estimation and model refinement using monocular camera - ambiguity limitation by inequality constraints. Proc. Int. Workshop on Automatic Face and Gesture Recognition, 268-273. 1998.
  • [11] Starner T., Pentland A.: Real-time american sign language recognition using desk and wearable computer based video. IEEE Trans. on PAMI, 20(12). 1998.
  • [12] Holden E., Owens O.: Visual sign language recognition. R. Klette, T. Huang, G. Gimen'garb (Eds.): Multi-Image Search and Analysis. LNCS 2032, Springer, Berlin. 2000.
  • [13] O'Hagan R.G.: Vision-Based Gesture Recognition as an Interface to Virtual Environment. PhD thesis, Australian National University. 2002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA1-0003-0022
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.