Browse > Article
http://dx.doi.org/10.5392/IJoC.2011.7.4.024

Bottle Label Segmentation Based on Multiple Gradient Information  

Chen, Yanjuan (School of Electronics and Computer Engineering Chonnam National University)
Park, Sang-Cheol (School of Electronics and Computer Engineering Chonnam National University)
Na, In-Seop (School of Electronics and Computer Engineering Chonnam National University)
Kim, Soo-Hyung (School of Electronics and Computer Engineering Chonnam National University)
Lee, Myung-Eun (Medical Research Center, Seoul National University)
Publication Information
International Journal of Contents / v.7, no.4, 2011 , pp. 24-29 More about this Journal
Abstract
In this paper, we propose a method to segment the bottle label in images taken by mobile phones using multi-gradient approaches. In order to segment the label region of interest-object, the saliency map method and Hough Transformation method are first applied to the original images to obtain the candidate region. The saliency map is used to detect the most salient area based on three kinds of features (color, orientation and illumination features). The Hough Transformation is a technique to isolated features of a particular shape within an image. Therefore, we utilize it to find the left and right border of the bottle. Next, we segment the label based on the gradient information obtained from the structure tensor method and edge method. The experimental results have shown that the proposed method is able to accurately segment the labels as the first step of product label recognition system.
Keywords
Hough Transformation; Saliency Map; Local Structure Tensor; Bottle Label Segmentation;
Citations & Related Records
연도 인용수 순위
  • Reference
1 B. C. Ko, and J. Y. Nam, "Object-of-interest image segmentation based on human attention and semantic region," Optical Society of Society of America, Vol. 23. Oct. 2006, pp. 2462-2470.   DOI   ScienceOn
2 L. Itti, C. Koch, and E. Niebur, "A Model of Saliecny-Based Visual Attention for Rapid Scene Analysis", IEEE Trans. Pattern Anal. Mach. Intell. 20, 1998, 1254-1259.   DOI   ScienceOn
3 D. Ballard, "Generalizing the Hough Transform to Detection Arbitray Shape," Pattern Recognition Vo. 13, No. 2, 1981. pp. 111-122.   DOI   ScienceOn
4 R. Duda and P. Hart, "Use of the Hough Transformation to Detect Line and Curves in picutures," Communication of the ACM, Vol. 15, No. 1, Jan. 1972, pp. 11-15.   DOI
5 H. Knutsson, "Representing Local Structure Using Tensor," Proceeding of the 6th Scandinavian Conf. on Image Analysis. 1989. pp. 244-251.
6 R. C. Gonzalez, E. W. and S. L. Eddins, Digital Image Processing using MATLAB, Publishing House of Electronics Industry, 2002
7 S. W. Hong. L. Choi, "Automatic Flowers Recognition Using Segmentation," Korea Computer Congress, Vol. 38, No. 1(A), 2011, pp. 463-465.
8 J. S. Lee, S. H. Kim, and J. H Park, G. S Lee, H. J Yang, C. W. Lee, "Recognition of Text in Wine Label Images," IEEE Pattern Recognition on Chinese Conference, 2009, pp. 1-5.
9 N. Otsu., "A Threshold Selection Method from Gray- Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.   DOI   ScienceOn
10 J. B. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations," Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1967, pp. 281-297
11 T. F. Chan and L. A. Vese, "Active Contours Without Edge," IEEE Transactions on Image Processing, Vol. 10, No. 2, 2001, pp. 266-277.   DOI   ScienceOn
12 M. Rousson, and R. Seriche, A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images, Proceeding of IEEE Workshop on Motion and Video Computing, 2002.
13 J. B. Shi and J. D. Malik, "Normalized Cuts and Image Segmentation," IEEE Transactions on pattern analysis and machine intelligence, Vol. 22, No. 8, 2000, pp. 888-905.   DOI   ScienceOn
14 Y. Boykov, Vladimir Kolmogorov, "An Experimental Comparison of Min-Cut/Flow Algorithms for Energy Minimization in Vision," IEEE Transactions on pattern analysis and machine intelligence, Vol. 26, No. 9, 2004, pp. 1124-1137.   DOI   ScienceOn