Hybrid Document Matching Method for Page Identification of Digilog Books

  • Jonghee Park
  • Woontack Woo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7220)

Abstract

Digilog Books are AR (Augmented Reality) books, which provide additional information by visual, haptic, auditory, and olfactory senses. In this paper, we propose an accurate and adaptive feature matching method based on a page layout for the Digilog Books. While previous Digilog Books attached visual markers or matched natural features extracted from illustrations for page identification, the proposed method divides input images, captured by camera, into text and illustration regions using CRLA (Constrained Run Length Algorithm) according to the page layouts. We apply LLAH (Locally Likely Arrangement Hashing) and FAST+SURF (FAST features using SURF descriptor) algorithm to appropriate region in order to get a high matching rate. In addition, it merges matching results from both areas using page layout in order to cover large area. In our experiments, the proposed method showed similar matching performance with LLAH in text documents and FAST+SURF in illustrations. Especially, the proposed method showed 15% higher matching rate than LLAH and FAST+SURF in the case of documents that contain both text and illustration. We expect that the proposed method would be applicable to identifying various documents for diverse applications such as augmented reality and digital library.

Keywords

Document matching augmented reality Digilog Book page identification 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Billinghurst, M., Kato, H., Poupyrev, I.: The magicbook-moving seamlessly between reality and virtuality. IEEE Computer Graphics and Applications 21(3), 6–8 (2001)Google Scholar
  3. 3.
    Chum, O., Matas, J.: Matching with PROSAC-progressive sample consensus. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 220–226. IEEE (2005)Google Scholar
  4. 4.
    Ha, T., Lee, Y., Woo, W.: Digilog book for temple bell tolling experience based on interactive augmented reality. Virtual Reality, 1–15 (2010)Google Scholar
  5. 5.
    Kato, H., Billinghurst, M.: Marker tracking and hmd calibration for a video-based augmented reality conferencing system. In: Proc. 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR 1999), October 20-21, pp. 85–94 (1999)Google Scholar
  6. 6.
    Kim, K., Lepetit, V., Woo, W.: Scalable real-time planar targets tracking for digilog books. The Visual Computer 26(6), 1145–1154 (2010)CrossRefGoogle Scholar
  7. 7.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Nakai, T., Kise, K., Iwamura, M.: Use of Affine Invariants in Locally Likely Arrangement Hashing for Camera-Based Document Image Retrieval. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 541–552. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Rosten, E., Drummond, T.W.: Machine Learning for High-Speed Corner Detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Taketa, N., Hayashi, K., Kato, H., Noshida, S.: Virtual Pop-Up Book Based on Augmented Reality. In: Smith, M.J., Salvendy, G. (eds.) HCII 2007, Part II. LNCS, vol. 4558, pp. 475–484. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Uchiyama, H., Saito, H.: Augmenting Text Document by On-Line Learning of Local Arrangement of Keypoints. In: Proc. 8th IEEE/ACM International Symposium on Mixed and Augmented Reality ISMAR 2009, pp. 95–98 (2009)Google Scholar
  12. 12.
    Wagner, D., Reitmayr, G., Mulloni, A., Drummond, T., Schmalstieg, D.: Pose tracking from natural features on mobile phones. In: Proc. 7th IEEE/ACM International Symposium on Mixed and Augmented Reality ISMAR 2008, September 15-18, pp. 125–134 (2008)Google Scholar
  13. 13.
    Wahl, F., Wong, K., Casey, R.: Block segmentation and text extraction in mixed text/image documents. Computer Graphics and Image Processing 20(4), 375–390 (1982)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jonghee Park
    • 1
  • Woontack Woo
    • 2
  1. 1.GIST U-VR Lab.GwangjuS. Korea
  2. 2.KAIST GSCT UVR LabDaejeonKorea

Personalised recommendations