A Fast Detection Method for Bottle Caps Surface Defect Based on Sparse Representation - Intelligent Computing for Sustainable Energy and Environment - page 84

dictionary. The closest match position can be found by searching the template
dictionary. Experiments on real world images of bottle caps surface show that
the proposed method is the effectiveness in searching the defect of caps rotated
by arbitrary angles. However, our method is more sensitive to changes in light
and in printing density. Our future work is to improve the algorithm so that it
is robust to changes in brightness and printing density.
Acknowledgments. This work was supported in part by the National Natu-
ral Science Foundation of China under Grant No.61074032 and No.61104089,
Science and Technology Commission of Shanghai Municipality under Grant
No.10JC1405000.
References
1. Kumar, A.: Computer-vision-based fabric defect detection: A survey. IEEE Trans-
actions on Industrial Electronics 55(1), 348-363 (2008)
2. Todoroki, C.L., Lowell, E.C., Dykstra, D.: Automated knot detection with visual
post-processing of Douglas-fir veneer images. Computers and Electronics in Agri-
culture 70(1), 163-171 (2010)
3. Yazdi, L., Prabuwono, A.S., Golkar, E.: Feature extraction algorithm for fill level
and cap inspection in bottling machine. IEEE (2011)
4. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine
Learning 29(2), 131-163 (1997)
5. Kleinbaum, D.G., Klein, M.: Maximum likelihood techniques: An overview. Logistic
Regression, 103-127 (2010)
6. Ullah, F., Kaneko, S.: Using orientation codes for rotation-invariant template
matching. Pattern Recognition 37(2), 201-209 (2004)
7. Starck, J.L., Elad, M., Donoho, D.L.: Image decomposition via the combination
of sparse representations and a variational approach. IEEE Transactions on Image
Processing 14(10), 1570-1582 (2005)
8. Donoho, D.L.: For most large underdetermined systems of linear equations the
minimal-norm solution is also the sparsest solution. Communications on Pure and
Applied Mathematics 59(6), 797-829 (2006)
9. Mei, X., Ling, H.B.: Robust Visual Tracking and Vehicle Classification via Sparse
Representation. IEEE Transactions on Pattern Analysis and Machine Intelli-
gence 33(11), 2259-2272 (2011)
10. Huang, J.B., Yang, M.H.: Fast sparse representation with prototypes. In: Proceed-
ings of the IEEE Computer Society Conference on Computer Vision and Pattern
Recognition, San Francisco, CA, United States, pp. 3618-3625 (2010)