A Laplacian energy for document binarization

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Volumn , Issue , 2011, Pages 6-10

  Howe, Nicholas R ^a  

^a SMITH COLLEGE   (United States)

Author keywords

I.4.0.b Image processing software; I.4.6.b Graph theoretic methods; I.4.6.d Pixel classification

Indexed keywords

A-LAPLACIAN; BINARIZATIONS; CANNY EDGE DETECTION; GRAPH CUT; GRAPH-THEORETIC METHODS; I.4.6.D PIXEL CLASSIFICATION; IMAGE-PROCESSING SOFTWARE; LAPLACIAN OPERATOR; LOCAL LIKELIHOOD; MINIMUM ENERGY; OBJECTIVE FUNCTIONS; SEGMENTATION METHODS;

ALGORITHMS; EDGE DETECTION; GRAPH THEORY; IMAGE SEGMENTATION; LAPLACE EQUATION; MATHEMATICAL OPERATORS;

LAPLACE TRANSFORMS;

EID: 82355162897     PISSN: 15205363     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDAR.2011.11     Document Type: Conference Paper

References (16)

1. Y. Boykov and V. Kolmogorov. IEEE trans. on pattern analysis and machine intelligence. An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision, 26(9):1124-1137, September 2004.
2. J. Canny. A computational approach to edge detection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(6):679-714, 1986.
3. B. Gatos, K. Ntirogiannis, and I. Pratikakis. Icdar 2009 document image binarization contest (dibco 2009). In Int. Conf. on Document Analysis and Recognition, pages 1375-1382, 2009.
4. B. Gatos, I. Pratikakis, and S. J. Perantonis. Efficient binarization of historical and degraded document images. In Int. Workshop on Document Analysis Systems, pages 447-454, 2008.
5. J. He, Q. D. M. Do, A. C. Downton, and J. H. Kim. A comparison of binarization methods for historical archive documents. In Int. Conf. on Document Analysis and Recognition, pages 538-542, 2005.
6. J. G. Kuk and N. I. Cho. Feature based binarization of document images degraded by uneven light condition. In Int. Conf. on Document Analysis and Recognition, pages 748-752, 2009.
7. J. G. Kuk, N. I. Cho, and K. M. Lee. Map-mrf approach for binarization of degraded document image. In Proc. Int. Conf. on Image Proc., pages 2612-2615, 2008.
8. J.C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM Journal of Optimization, 9(1):112-147, 1998.
9. R. Milewski and V. Govindaraju. Binarization and cleanup of handwritten text from carbon copy medical form images. Pattern Recognition, 41(4):1308-1315, 2008. (Pubitemid 350218148)
10. W. Niblack. An Introduction to Digital Image Processing. Prentice- Hall, Englewood Cliffs, New Jersey, 1986.
11. N. Otsu. A threshold selection method from graylevel histogram. IEEE Trans. on System, Man, Cybernetics, 19(1):62-66, January 1978.
12. P. Palumbo, P. Swaminathan, and S. Srihari. Document image binarization: Evaluation of algorithms. SPIE Applications of Digital Image Processing IX, 697:278-285, 1986. (Pubitemid 17609720)
13. I. Pratikakis, B. Gatos, and K. Nirogiannis. H-DIBCO 2010 - handwritten document image binarization competition. In 12th Int. Conf. on Frontiers in Handwriting Recognition, pages 727-732, 2010.
14. N. Sauvola and M. Pietikainen. Adaptive document image binarization. Pattern Recognition, 33(2):225-236, January 2000. (Pubitemid 32262190)
15. Y. Sun, B. Yuan, Z. Miao, and C. Wan. Better foreground segmentation for static cameras via new energy form and dynamic graph-cut. In Int. Conf. on Pattern Recognition, pages 49-52, 2006.
16. J. Wang, M. S. Brown, and C. L. Tan. Automatic corresponding control points selection for historical document image registration. In Int. Conf. on Document Analysis and Recognition, pages 1176-1180, 2009.