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Handwritten Image Segmentation by the Modified Area-based Region Selection Technique  

Hwang Jae-Ho (Dept. of Electronic Engineering, Hanbat National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.43, no.5, 2006 , pp. 30-36 More about this Journal
Abstract
In this paper, a new type of written image segmentation based on relative comparison of region areas is proposed. The original image is composed of two distinctive regions; information and background. Compared with this binary original image, the observed one is the gray scale which is represented with complex regions with speckles and noise due to degradation or contamination. For applying threshold or statistical approach, there occurs the region-deformation problem in the process of binarization. At first step, the efficient iterated conditional mode (ICM) which takes the lozenge type block is used for regions formation into the binary image. Secondly the information region is estimated through selecting action and restored its primary state. Not only decision of the attachment to a region but also the calculation of the magnitude of its area are carried on at each current pixel iteratively. All region areas are sorted into a set and selected through the decision parameter which is obtained statistically. Our experiments show that these approaches are effective on ink-rubbed copy image (拓本 'Takbon') and efficient at shape restoration. Experiments on gray scale image show promising shape extraction results, comparing with the threshold-segmentation and conventional ICM method.
Keywords
Image segmentation; Takbon; Region; ICM; MABRS;
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1 J. Park and L. Kurz, 'Image enhancement using modified ICM method,' IEEE Trans. on Image Processing, Vol. 5, no. 5, pp. 765-771, May 1996   DOI   ScienceOn
2 S. Krishnamachari and R. Chellappa, 'Multiresolution Gauss-Markov random field models for texture segmentation,' IEEE Trans. on Image Processing, Vol. 6, no. 2, pp. 251-267, Feb. 1997   DOI   ScienceOn
3 S. Foucher M. Germain, J. M. Boucher and G. B. Benie, 'Multisource classification using ICM and Dempster-Shafer theory,' IEEE trans. on Instru. and Measure., Vol. 51, no. 2, pp. 277-281, April 2002   DOI   ScienceOn
4 F. Destrempes, and M. Mignotte, 'A statistical model for contours in images,' IEEE Trans. Pattern Anal. Machine Intell., Vol. 26, no. 5, pp. 626-638, May 2004   DOI   ScienceOn
5 Y. Solihin and C. G. Leedham, 'Interal ratio: A new class of global thresholding techniques for handwriting images,' IEEE Trans. Pattern Anal. Machine Intell., Vol. 21, no. 8, pp. 761-768, August 1999   DOI   ScienceOn
6 M. M. Chang, A. M. Tekalp and M. I. Sezan, 'Simultaneous motion estimation and segmentation,' IEEE Trans. on Image Processing, Vol. 6, no. 9, pp. 1326-1333, Sept. 1997   DOI   ScienceOn
7 J. Besag, 'On the statistical analysis of dirty pictures,' J. R. Statist. Soc., Vol. 48, no. 3, pp. 259-302, 1986
8 J. K. Fwu and P. M. Djuric, 'Unsupervised vector image segmentation by a tree structure ICM algorithm,' IEEE Trans. Medical Imaging, Vol. 15, no. 6, pp. 871-880, Dec. 1996   DOI   ScienceOn
9 G. S. R. Fjortoft and A. H. S. Solberg, 'A bayesian approach to classification of multiresolution remote sensing data,' IEEE Trans. Geosci. Remote Sens., Vol. 43, no. 3, pp. 539-547, Mar. 2005   DOI   ScienceOn
10 http://myhome.shinbiro.com/~kbyon/petro/takbon.htm
11 Xiaoyi Jiang, D. Mojon, 'Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images,' IEEE Trans. Pattern Anal. Machine Intell., Vol. 25, no. 1, pp. 131-137, Jan., 2003   DOI   ScienceOn
12 S. Geman, and D. Geman, 'Stochastic relaxation gibbs distributions and the bayesian restoration of images,' IEEE Trans. Pattern Anal. Machine Intell., Vol. PAMI-6, no. 6, pp. 721-740, 1984   DOI