Advanced SearchSearch Tips
Bottle Label Segmentation Based on Multiple Gradient Information
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Bottle Label Segmentation Based on Multiple Gradient Information
Chen, Yanjuan; Park, Sang-Cheol; Na, In-Seop; Kim, Soo-Hyung; Lee, Myung-Eun;
  PDF(new window)
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.
Hough Transformation;Saliency Map;Local Structure Tensor;Bottle Label Segmentation;
 Cited by
관심영역 추정 및 색상/거리 특징기반 분류기를 이용한 꽃 영역 자동 분할,오강한;김수형;나인섭;

한국정보과학회논문지:소프트웨어및응용, 2012. vol.39. 9, pp.759-766
S. W. Hong. L. Choi, "Automatic Flowers Recognition Using Segmentation," Korea Computer Congress, Vol. 38, No. 1(A), 2011, pp. 463-465.

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.

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. crossref(new window)

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

T. F. Chan and L. A. Vese, "Active Contours Without Edge," IEEE Transactions on Image Processing, Vol. 10, No. 2, 2001, pp. 266-277. crossref(new window)

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.

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

D. Ballard, "Generalizing the Hough Transform to Detection Arbitray Shape," Pattern Recognition Vo. 13, No. 2, 1981. pp. 111-122. crossref(new window)

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. crossref(new window)

H. Knutsson, "Representing Local Structure Using Tensor," Proceeding of the 6th Scandinavian Conf. on Image Analysis. 1989. pp. 244-251.

R. C. Gonzalez, E. W. and S. L. Eddins, Digital Image Processing using MATLAB, Publishing House of Electronics Industry, 2002