An Error Analysis on Images Using Skeletonization Methods

Abstract:

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Vectorization is the most fundamental operation in interpretation of line drawings and document analysis. There are several reasons for converting image vectorization. Vector data is normally created from existing natural source image like photographs,scanned images. Choosing a best vectorization method that suits the needs of the system is very important. In general, good methods must preserve information like line geometry and intersection junction as far as possible. It is also important to analyze the error and find the accuracy of the result with respect to the original data. We have compared Skeletonization by Mathematical Morphology and Voronoi Diagrams with original image for vectorizing images. Root mean squre error is one of the good methods to analysis an error on original Image, Mathematical Morphology and Voronoi Diagrams. Literature about above said methods is also included in this paper.

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Edited by:

Li Yuan

Pages:

4184-4188

DOI:

10.4028/www.scientific.net/AMR.403-408.4184

Citation:

N. Daryal and V. Kumar, "An Error Analysis on Images Using Skeletonization Methods", Advanced Materials Research, Vols. 403-408, pp. 4184-4188, 2012

Online since:

November 2011

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$38.00

[1] Deutsch E.S. (1972). Thinning algorithms on rectangular, hexagonal and triangular arrays. Communications of the ACM, 15, 827-837.

DOI: 10.1145/361573.361583

[2] Tamura H. (1978). A Comparison of line thinning algorithms from digital geometry viewpoint. In Proceedings of the 4th International Conference on Pattern Recognition, Kyoto, Japan, 715-719.

[3] Smith R.W. (1978). Computer processing of line images: A survey. Pattern Recognition x, 20(1), 7-15.

[4] Davies E.R. & Plummer A.P.N. (1981). Thinning algorithms: a critique and a new methodology. Pattern Recognition, 14, 53-53.

DOI: 10.1016/0031-3203(81)90045-5

[5] Serra J. (1982). Image Analysis and Mathematical Morphology, I, II. Academic Press.

[6] Watson L.T., Arvind K., Ehrich R.W. & Haralick R.M. (1984). Extraction of lines and regions from greytone line drawing images. Pattern Recognition, 17, 493-507.

DOI: 10.1016/0031-3203(84)90047-5

[7] Di Zenzo S. & Morelli A. (1989) A useful image representation. In Proceedings of the 5th International Conference on Image Analysis and Processing, Word Scientific Publishing, Singapore, 170-178.

[8] Eastman C.M. (1990). Vector versus raster. A functional comparison of drawings technologies. In IEEE Computer Graphics and Applications, 68–80.

DOI: 10.1109/38.59039

[9] Boatto L et al (1992) An Interpretation System for Land Register Images. IEEE Computer, 25(7), 25-32.

[10] Lam L, Lee SW & Suen CY (1992). Thinning methodologies - A comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(9), 869-887.

DOI: 10.1109/34.161346

[11] Rafael C. Gonzalez, Richard E. Woods, Steven L. & Eddins (2007). Digital Image Processing using MATLAB. Pearson Prentice hall, Pearson Education Inc.

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