Sage Journals: Discover world-class research

Abstract

Fiber cross-sectional shapes can influence many physical properties of fibers. Automated identification of shaped fibers is critically important for fiber quality inspection. This paper presents a distance-based skeletonization algorithm used for reliable identification of shaped fibers. The skeleton of a fiber cross section, which is generated from fiber distance maps and maximal disks, is ensured to be continuous and insensitive to edge noise, and therefore can be used as abstract representations of fiber topology for shape analysis. A set of shape descriptors are defined from fiber skeletons and a support vector machine method is used to classify fibers based on the shape measurements. The experimental results show that the presented approach can be used to recognize shaped fibers based on the analysis of skeleton structures.

Keywords

fiber’s cross-section skeleton support vector machine identification

Get full access to this article

View all access options for this article.

References

Gonzalez, R.C. , and Woods, R.E. , "Digital Image Processing", Prentice Hall, Englewood Cliffs, NJ, 2007.

Arcelli, C. , and Baja, G.S.D. , A Width-independent Fast Thinning Algorithm, IEEE Trans. Pattern Anal. Machine Intell., 7(4), 463-474 ( 1985).

Shih, F.Y. , and Pu, C.C. A Skeletonization Algorithm by Maximal Tracking on Euclidean Distance Transform , Pattern Recog., 28, 331-341 (1995).

Blum, H. , Biological Shape and Visual Science : Part I, Theoret. Biol., 38, 205-287 (1973).

Blum, H. , and Nagel, R.N. , Shape Description Using Weighted Symmetric Axis Features. Pattern Recog., 10, 167- 180 (1978).

Blum, H.A. , Transformation for Extracting New Descriptors of Shape, in "Models for the Perception of Speech and Visual Form", Walthen-Dunn, W. (ed), 1967.

Lam, L. , and Suen, C.Y. , An Evaluation of Parallel Thinning Algorithms for Character-Recognition, IEEE Trans. Pattern Anal. Machine Intell., 17(9), 914-919 (1995).

Rosenfeld, A. , and Pfaltz, J. , Sequential Operations in Digital Picture Processing. J. ACM, 13(4), 471-494 (1966).

Russ, J.C. , "Image Processing Handbook", 2nd edn, CRC Press, 1995.

10.

Hirata, T.A. , Unified Linear-time Algorithm for Computing Distance Maps, Inform. Process. Lett., 58, 129-133 ( 1996).

11.

Ge, Y. , and Fitzpatrick, J.M. , On the Generation of Skeletons from Discrete Euclidean Distance Maps, IEEE Trans. Pattern Anal. Machine Intell., 18, 1055-1066 (1996).

12.

Nillsson, F. , and Danielsson, P. , Finding the Minimal Set of Maximum Disks for Binary Objects, Graph. Model. Image Process., 59, 55-60 (1997).

13.

Niblack, W. , Gibbons, P.B. , and Capson, D. , Generating Connected Skeletons for Exact and Approximate Reconstruction, in "Proceedings CVPR '92", 1992, pp. 826-828.

14.

Vapnik, V. , "The Nature of Statistical Learning Theory", Springer-Verlag , New York, 1995.

15.

Hsu, C. , Chang, C. , and Lin C. , "A Practical Guide to Support Vector Classification", http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf, 2008.

16.

Boylston, E.K. , Hinojosa, O. , and Herbert, J.J. , A Quick Embedding Method for Light and Electron Microscopy of Textile Fibers, J. Biotechnic. Histochem. , 66(3), 122-124 (1991).

17.

Wan, Y. , Yao, L. , Xu, B. , and Wu, X. , Separation of Clustered Fibers in Cross-Sectional Images Using Image Set Theory, Textile Res. J., accepted.

Shaped Fiber Identification Using a Distance-Based Skeletonization Algorithm

Abstract

Keywords

Get full access to this article

References