JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Label Restoration Using Biquadratic Transformation
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Label Restoration Using Biquadratic Transformation
Le, Huy Phat; Nguyen, Toan Dinh; Lee, Guee-Sang;
  PDF(new window)
 Abstract
Recently, there has been research to use portable digital camera to recognize objects in natural scene images, including labels or marks on a cylindrical surface. In many cases, text or logo in a label can be distorted by a structural movement of the object on which the label resides. Since the distortion in the label can degrade the performance of object recognition, the label should be rectified or restored from deformations. In this paper, a new method for label detection and restoration in digital images is presented. In the detection phase, the Hough transform is employed to detect two vertical boundaries of the label, and a horizontal edge profile is analyzed to detect upper-side and lower-side boundaries of the label. Then, the biquadratic transformation is used to restore the rectangular shape of the label. The proposed algorithm performs restoration of 3D objects in a 2D space, and it requires neither an auxiliary hardware such as 3D camera to construct 3D models nor a multi-camera to capture objects in different views. Experimental results demonstrate the effectiveness of the proposed method.
 Keywords
label restoration;biquadratic transformation;Hough transform;
 Language
English
 Cited by
 References
1.
Qixiang Ye, Jianbin Jiao, Jun Huang, Hua Yu, “Text detection and restoration in natural scene images”, Journal of Visual Communication and Image Representation, vol.18, no.6, Dec. 2007, pp.504-513. crossref(new window)

2.
Wu Guo-Ping, Ao Min-Si, Cheng Shi, Lei Hui, “Slant correction of Vehicle License Plate Image”, International Conference of Image Analysis and Processing, 2005, pp.919-922.

3.
Q. L Gu, C. Y. Suen, T. D. Bui and Z. C. Li, “Mathematical methods for font generation and shape design of characters”, Proc. Int. Conf. Comput. Process. Chinese Oriental Languages, 1991, pp. 1-14.

4.
Z. C. Li, T. D. Bui, C. Y. Suen, Y. Y. Tang and Q. L. Gu, “Splitting-integrating method for normalizing images by inverse transformations”, IEEE Trans. Pattern Anal. Mach. Intell. vol.16, no.6, 1992, pp. 678-686. crossref(new window)

5.
Z.C. Li, Q. L. Gu, C. Y. Suen and T. D. Bui, “A comparative study of nonlinear shape models for digital processing and pattern recognition, IEEE Trans. Syst. Man. Cybernet. vol.20, no.4, 1990, pp. 858-871. crossref(new window)

6.
Y.Y. Tang, Z. C. Li, C. Y. Suen and T. D. Bui, “Conversion of Chinese characters by transformation models”, Proc. Int. Conf. Comput. Process. Chinese Oriental Languages, 1988, pp. 293-297.

7.
Y. Y. Tang and C. Y. Suen, “Nonlinear shape restoration by transformation models”, Int. Conf. Pattern Recognition, 1990, pp. 14-19. crossref(new window)

8.
R. Duda and P. Hart. “Use of the Hough transform to detect lines and curves in pictures”, Communications of the ACM, Jan. 1972, pp.11-15 crossref(new window)

9.
Rafael C. Gonzalez Richard E. Woods. Digital Image Processing, Pearson, New Jersey, 2010

10.
James D. Foley et. al, Computer Graphics Principles and Practices in C, 2nd Edition, Addison Wesley, 1997

11.
Y.Y. Tang and C.Y. Suen, “Image transformation approach to nonlinear shape restoration”, IEEE Trans. Syst. Man Cybernet. Vol.23, no.1, 1993, pp. 155–172. crossref(new window)