Bottle Label Segmentation Based on Multiple Gradient Information
2
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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.
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.
참고문헌 (14)
- S. W. Hong, "Automatic Flowers Recognition Using Segmentation", Korea Computer Congress, 38(1), pp.463~465 (2011)
- J. S. Lee, "Recognition of Text in Wine Label Images", IEEE Pattern Recognition on Chinese Conference, pp.1~5 (2009)
- N. Otsu., "A Threshold Selection Method from Gray-Level Histograms", Man, and Cybernetics, 9(1), pp.62~66 (1979)
- J. B. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations", pp.281~297 (1967), University of California Press
- T. F. Chan, "Active Contours Without Edge", IEEE Transactions on Image Processing, 10(2), pp.266~277 (2001)
- M. Rousson, "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, "Normalized Cuts and Image Segmentation", IEEE Transactions on pattern analysis and machine intelligence, 22(8), pp.888~905 (2000)
- Y. Boykov, "An Experimental Comparison of Min-Cut", Flow Algorithms for Energy Minimization in Vision, 2004, pp.26(9), 1124~1137
- B. C. Ko, "Object-of-interest image segmentation based on human attention and semantic region", Optical Society of Society of America, 23, pp.2462~2470 (2006)
- L. Itti, "A Model of Saliecny-Based Visual Attention for Rapid Scene Analysis", IEEE Trans. Pattern Anal. Mach. Intell, 20, pp.1254~1259 (1998)
- D. Ballard, "Generalizing the Hough Transform to Detection Arbitray Shape", Pattern Recognition, 13(2), pp.111~122 (1981)
- R. Duda, "Use of the Hough Transformation to Detect Line and Curves in picutures", Communication of the ACM, 15(1), pp.11~15 (1972)
- H. Knutsson, "Representing Local Structure Using Tensor", Proceeding of the 6th Scandinavian Conf. on Image Analysis, pp.244~251 (1989)
- R. C. Gonzalez, "Digital Image Processing using MATLAB" (2002), House of Electronics Industry
