Machine Vision and Applications

, Volume 24, Issue 6, pp 1213–1227 | Cite as

Learning class-specific dictionaries for digit recognition from spherical surface of a 3D ball

Original Paper
  • 264 Downloads

Abstract

In the literature, very few researches have addressed the problem of recognizing the digits placed on spherical surfaces, even though digit recognition has already attracted extensive attentions and been attacked from various directions. As a particular example of recognizing this kind of digits, in this paper, we introduce a digit ball detection and recognition system to recognize the digit appearing on a 3D ball. The so-called digit ball is the ball carrying Arabic number on its spherical surface. Our system works under weakly controlled environment to detect and recognize the digit balls for practical application, which requires the system to keep on working without recognition errors in a real-time manner. Two main challenges confront our system, one is how to accurately detect the balls and the other is how to deal with the arbitrary rotation of the balls. For the first one, we develop a novel method to detect the balls appearing in a single image and demonstrate its effectiveness even when the balls are densely placed. To circumvent the other challenge, we use spin image and polar image for the representation of the balls to achieve rotation-invariance advantage. Finally, we adopt a dictionary learning-based method for the recognition task. To evaluate our system, a series of experiments are performed on real-world digit ball images, and the results validate the effectiveness of our system, which achieves 100 % accuracy in the experiments.

Keywords

Digit ball recognition Dictionary learning Sparse coding Circle detection Rotation invariance 

References

  1. 1.
    Aharon, M., Elad, M., Bruckstein, A.: The k-svd: An algorithm for designing of overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)CrossRefGoogle Scholar
  2. 2.
    Bradley, D.M., Bagnell, J.A.: Differentiable sparse coding. Adv. Neural Inform. Process. Syst. (NIPS) (2008)Google Scholar
  3. 3.
    Breiman, L.: Better subset regression using the nonnegative garrote. Technometrics 37(4), 373–384 (1995)MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Cheng, L., Wang, D., Deng, X., Kong, S.: Sparse representation for three-dimensional number ball recognition. In: WRI Global Congress on Intelligent Systems (GCIS) (2010)Google Scholar
  5. 5.
    Elad, M., Aharon, M.: Image denoising via learned dictionaries and sparse representation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2006)Google Scholar
  6. 6.
    Gader, P.D., Khabou, M.A.: Automatic feature generation for handwritten digit recognition. IEEE Trans. Pattern Anal. Mach. Intell. 18(12), 1256–1261 (1996)CrossRefGoogle Scholar
  7. 7.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Inform. Theory 8(2), 179–187 (1962)MATHCrossRefGoogle Scholar
  8. 8.
    Huang, T., Wang, D., Cheng, L., Deng, X.: Number ball recognition at arbitrary pose using multiple view instances. In: IEEE Youth Conference on Information, Computing and Telecommunication, pp. 510–513 (2009)Google Scholar
  9. 9.
    Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)CrossRefGoogle Scholar
  10. 10.
    Kong, S., Wang, D.: A dictionary learning approach for classification: separating the particularity and the commonality. In: European Conference on Computer Vision (ECCV) (2012)Google Scholar
  11. 11.
    Kupeev, K.Y., Wolfson, H.J.: A new method of estimating shape similarity. Pattern Recognit. Lett. 17(8), 873–887 (1996)CrossRefGoogle Scholar
  12. 12.
    Kurita, T., Hotta, K., Mishima, T.: Scale and rotation invariant recognition method using higher-order local autocorrelation features of log-polar image. In: Asian Conference on Computer Vision (ACCV), pp 89–96 (1998)Google Scholar
  13. 13.
    Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1265–1278 (2005)CrossRefGoogle Scholar
  14. 14.
    Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Advanced in Neural Information Processing systems (NIPS) (2007)Google Scholar
  15. 15.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  16. 16.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Supervised dictionary learning. In: Advanced in Neural Information Processing systems (NIPS) (2008)Google Scholar
  17. 17.
    Ojala, T., Pietikainen, M., Maenpa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  18. 18.
    Rigamonti, R., Brown, M.A., Lepetit, V.: Are sparse representations really relevant for image classification? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)Google Scholar
  19. 19.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58(1), 267–288 (1996)Google Scholar
  20. 20.
    Trier, O.D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition: a survey. Pattern Recognit. 29(4), 641–662 (1996)CrossRefGoogle Scholar
  21. 21.
    Vizireanu, D.N.: Generalizations of binary morphological shape decomposition. J. Electron. Imag. 16(1), 1–6 (2007)CrossRefGoogle Scholar
  22. 22.
    Vizireanu, D.N.: Morphological shape decomposition interframe interpolation method. J. Electron. Imag. 17(1), 1–5 (2008)CrossRefGoogle Scholar
  23. 23.
    Vizireanu, D.N., Halunga, S., Marghescu, G.: Morphological skeleton decomposition interframe interpolation method. J. Electron. Imag. 19(2), 1–3 (2010)CrossRefGoogle Scholar
  24. 24.
    Wang, D., Cui, C., Wu, Z.: Matching 3d models with global geometric feature map. In: International Conference on Multi-media Modelling (MMM) (2006)Google Scholar
  25. 25.
    Wang, D., Qian, H.: 3d object recognition by fast spherical correlation between combined view egis and pft. In: IAPR International Conference on Pattern Recognition (ICPR), pp. 1–4 (2008)Google Scholar
  26. 26.
    Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)Google Scholar
  27. 27.
    Yang, L., Albregtsen, F.: Fast computation of invariant geometric moments: A new method giving correct results. In: IAPR International Conference on Pattern Recognition (ICPR), pp. 201–204 (1994) Google Scholar
  28. 28.
    Yu, D., Yan, H.: Reconstruction of broken handwritten digits based on structural morphological features. Pattern Recognit. 34(2), 235–254 (1999)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: Which helps face recognition? In: IEEE International Conference on Computer Vision (ICCV) (2011)Google Scholar
  30. 30.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  31. 31.
    Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B 67(2), 301–320 (2005)MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyZhejiang UniversityHangzhouChina

Personalised recommendations