Shape Recognition Using Segmenting and String Matching
DOI:
https://doi.org/10.24203/ajas.v10i6.7138Keywords:
Shape recognition, string matching, curve, featureAbstract
This paper presents an efficient way to represent objects. The image of the object is converted into an edge image. Important points of the curve are identified by the dominant point detection method. A line segment of every two consecutive important points is a categorical line segment or a non-linear line segment. Nonlinear segments are fitted as circular arcs. In addition, the compactness of approximate polygons is used as a feature in the shape recognition process. Experimental results show that using this new global feature has better recognition performance than traditional features such as relative distance, length and angle. Overall the new method is efficient and effective in representing and recognizing shapes.
References
H. Bunke and U. Bühler, “Applications of approximate string matching to 2D shape recognition,” Pattern Recognition, vol. 26, no. 12, pp. 1797-1812, 1993.
C. C. Chang, S. M. Hwang, and D. J. Buehrer, “A shape recognition scheme based on relative distances of feature points from the centroid.” Pattern Recognition, vol. 24, pp. 1053-1063, 1991.
S. W. Chen, S. T. Tung, C. Y. Fang, S. Cherng, A. K. Jain, “Extended attributed string matching for shape recognition,” Computer Vision and Image Understanding, vol. 70, no. 1, pp. 36-50, 1998.
K. S. Fu, “A step towards unification of syntactic and statistical pattern recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 5, 200-205, 1983.
[S. Kaygin and M. M. Bulut, “Shape recognition using attributed string matching with polygon vertices as the primitives,” Pattern Recognition Letters, vol. 23, pp. 287-294, 2002.
[M. Maes, “On a cyclic string-to-string correction problem Information,” Processing Letters, vol. 35, pp. 73-78, 1990.
[M. Maes, “Polygonal shape recognition using string-matching techniques,” Pattern Recognition, vol. 24, pp. 433-440, 1991.
[D. Sankoff and J. B. Kruskal (ed.), Time Warps, String Edits and Micromolecules: The Theory and Practice of Sequence Comparison, Addison Wesley, Reading, MA, 1983.
[W. H. Tsai and K. S. Fu, “Attributed grammar- a tool for combining syntactic and statistical approaches to pattern recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, pp. 873-885, 1980.
[W. H. Tsai and S. S. Yu, “Attributed string matching with merging for shape recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 7, pp. 453-462, 1985.
Y. T. Tsay and W. H. Tsai, “Model-guided attributed string matching by split-and-merge for shape recognition,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 3, pp. 159-179, 1989.
R. A. Wagner and M. J. Fischer, “The string-to-string correction problem,” J. ACM, vol. 21, pp. 168-173, 1974.
C. K. Wong and A. K. Chandra, “Bounds for the string-editing problem,” J. ACM, vol. 23, pp.13-16, 1976.
W. Y. Wu, “A simple method for dominant point detection,” Imaging Science Journal, vol. 49, pp. 125-133, 2001.
W. Y. Wu and M. J. J. Wang, “Two-dimensional object recognition through two-stage string matching,” IEEE Trans. Image Processing, vol. 8, no. 7, pp. 978-981, 1999.
K. C. You and K. S. Fu, “A syntactic approach to shape recognition using attributed grammars,” IEEE Trans. Systems, Man, and Cybernetics, vol. 9, pp. 334-345, 1979.
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