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Increasing the Effect of Fingers in Fingerspelling Hand Shapes by Thick Edge Detection and Correlation with Penalization

  • Oğuz Altun
  • Songül Albayrak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)

Abstract

Fingerspelling is used in sign language to spell out names of people and places for which there is no sign or for which the sign is not known. In this work we describe a method for increasing the effect of fingers in Fingerspelling hand shapes. Hand shape objects are obtained by extraction of representative frames, color segmentation in YCrCb space and angle of least inertia based fast alignment [1]. Thick edges of the hand shape objects are extracted with a distance to edge based method. Finally a calculation that penalizes similarity for not-corresponding pixels is employed to correlation based template matching. The experimental Turkish fingerspelling recognition system recognizes all 29 letters of the Turkish alphabet. The train video database is created by three signers, and has a set of 290 videos. The test video database is created by four signers, and has a set of 203 videos. Our methods achieve a success rate of 99%.

Keywords

Turkish Fingerspelling Recognition Fast Alignment Angle of orientation Axis of Least Inertia Thick Edges Correlation with Penalization 

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References

  1. 1.
    Altun, O., Albayrak, S., Ekinci, A., Bükün, B.: Turkish Fingerspelling Recognition System Using Axis of Least Inertia Based Fast Alignment. In: The 19th Australian Joint Conference on Artificial Intelligence, AI 2006 (2006)Google Scholar
  2. 2.
  3. 3.
  4. 4.
    Starner, T., Weaver, J., Pentland, A.: Real-time American sign language recognition using desk and wearable computer based video. IEEE Transactions on Pattern Analysis and Machine Intelligence 20, 1371–1375 (1998)CrossRefGoogle Scholar
  5. 5.
    Holden, E.J., Lee, G., Owens, R.: Australian sign language recognition. Machine Vision and Applications 16, 312–320 (2005)CrossRefGoogle Scholar
  6. 6.
    Gao, W., Fang, G.L., Zhao, D.B., Chen, Y.Q.: A Chinese sign language recognition system based on SOFM/SRN/HMM. Pattern Recognition 37, 2389–2402 (2004)zbMATHCrossRefGoogle Scholar
  7. 7.
    Haberdar, H., Albayrak, S.: Real Time Isolated Turkish Sign Language Recognition From Video Using Hidden Markov Models With Global Features. In: Yolum, p., Güngör, T., Gürgen, F., Özturan, C. (eds.) ISCIS 2005. LNCS, vol. 3733, pp. 677–687. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Haberdar, H., Albayrak, S.: Vision Based Real Time Isolated Turkish Sign Language Recognition. In: International Symposium on Methodologies for Intelligent Systems, Bari, Italy (2006)Google Scholar
  9. 9.
    Lamar, M., Bhuiyant, M.: Hand Alphabet Recognition Using Morphological PCA and Neural Networks. In: International Joint Conference on Neural Networks, Washington, USA, pp. 2839–2844 (1999)Google Scholar
  10. 10.
    Rebollar, J., Lindeman, R., Kyriakopoulos, N.: A Multi-Class Pattern Recognition System for Practical Fingerspelling Translation. In: International Conference on Multimodel Interfaces, Pittsburgh, USA (2000)Google Scholar
  11. 11.
    Isaacs, J., Foo, S.: Hand Pose Estimation for American Sign Language Recognition. In: Thirty-Sixth Southeastern Symposium on IEEE System Theory, pp. 132–136 (2004)Google Scholar
  12. 12.
    Feris, R., Turk, M., Raskar, R., Tan, K.: Exploiting Depth Discontinuities for Vision-Based Fingerspelling Recognition. In: 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2004 (2004)Google Scholar
  13. 13.
    Sazonov, V., Vezhnevetsi, V., Andreeva, A.: A survey on pixel vased skin color detection techniques. In: Graphicon 2003, pp. 85–92 (2003)Google Scholar
  14. 14.
    Chai, D., Bouzerdom, A.: A Bayesian Approach To Skin Colour Classification. In: TENCON 2000 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Oğuz Altun
    • 1
  • Songül Albayrak
    • 1
  1. 1.Computer Engineering DepartmentYıldız Technical UniversityYıldız, İstanbulTürkiye

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