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Effective Multiresolution Arc Segmentation: Algorithms and Performance Evaluation
November 2004 (vol. 26 no. 11)
pp. 1491-1506
ASCII Text
x 
 
Jiqiang Song, Michael R. Lyu, Shijie Cai, "Effective Multiresolution Arc Segmentation: Algorithms and Performance Evaluation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1491-1506, November, 2004.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2004.103,
author = {Jiqiang Song and Michael R. Lyu and Shijie Cai},
title = {Effective Multiresolution Arc Segmentation: Algorithms and Performance Evaluation},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {26},
number = {11},
issn = {0162-8828},
year = {2004},
pages = {1491-1506},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2004.103},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Effective Multiresolution Arc Segmentation: Algorithms and Performance Evaluation
IS - 11
SN - 0162-8828
SP1491
EP1506
EPD - 1491-1506
A1 - Jiqiang Song,
A1 - Michael R. Lyu,
A1 - Shijie Cai,
PY - 2004
KW - Graphics recognition
KW - arc segmentation
KW - multiresolution
KW - circular tracking
KW - vectorization
KW - performance evaluation.
VL - 26
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Arc segmentation plays an important role in the process of graphics recognition from scanned images. The GREC arc segmentation contest shows there is a lot of room for improvement in this area. This paper proposes a multiresolution arc segmentation method based on our previous seeded circular tracking algorithm which largely depends on the OOPSV model. The newly-introduced multiresolution paradigm can handle arcs/circles with large radii well. We describe new approaches for arc seed detection, arc localization, and arc verification, making the proposed method self-contained and more efficient. Moreover, this paper also brings major improvement to the dynamic adjustment algorithm of circular tracking to make it more robust. A systematic performance evaluation of the proposed method has been conducted using the third-party evaluation tool and test images obtained from the GREC arc segmentation contests. The overall performance over various arc angles, arc lengths, line thickness, noises, arc-arc intersections, and arc-line intersections has been measured. The experimental results and time complexity analyses on real scanned images are also reported and compared with other approaches. The evaluation result demonstrates the stable performance and the significant improvement on processing large arcs/circles of the MAS method.

[1] D.A. Anderson, The Circular Structural Model J. Royal Statistical Soc., B, vol. 43, pp. 131-141, 1981.
[2] D.H. Ballard, Generalizing the Hough Transform to Detect Arbitrary Shapes Pattern Recognition, vol. 13, no. 2, pp. 111-122, 1981.
[3] M. Berman and D. Culpin, The Statistical Behavior of Some Least Squares Estimator of the Center and Radius of a Circle J. Royal Statistical Soc., B, vol. 48, pp. 183-196, 1986.
[4] P.L. Rosin and G.A. West, Segmentation of Edges into Lines and Arcs Image and Vision Computing, vol. 7, no. 2, pp. 109-114, May 1989.
[5] I. Amir, Algorithm for Finding the Center of Circular Fiducials Computer Vision, Graphics, Image Process, vol. 49, no. 3, pp. 398-406, 1990.
[6] V.A. Kovalevsky, New Definition and Fast Recognition of Digital Straight Segments and Arcs Proc. 10th Int'l Conf. Pattern Recognition (ICPR '90), vol. 2, pp. 31-34, 1990.
[7] L. Moura and R. Kitney, A Direct Method for Least-Squares Circle Fitting Computer Physics Comm., vol. 64, no. 1, pp. 57-63, 1991.
[8] V.F. Leavers, The Dynamic Generalized Hough Transform: Its Relationship to the Probabilistic Hough Transforms and an Application to the Concurrent Detection of Circles and Ellipses Computer Vision, Graphics, Image Understanding, vol. 56, no. 3, pp. 381-398, 1992.
[9] K.K.Y. Raymond, K.S.T. Peter, and N.K.L. Dennis, Modification of Hough Transform for Circles and Ellipses Detection Using a 2-Dimensional Array Pattern Recognition, vol. 25, no. 9, pp. 1007-1022, 1992.
[10] B.B. Chaudhori and P. Kundu, Optimum Circular Fit to Weighted Data in Multi-Dimensional Space Pattern Recognition Letters, vol. 14, no. 1, pp. 1-6, 1993.
[11] C. Ho and L. Chen, A Fast Ellipse/Circle Detector Using Geometric Symmetry Pattern Recognition, vol. 28, no. 1, pp. 117-124, 1995.
[12] Z. Wu, L. Wu, and A. Wu, The Robust Algorithms for Finding the Center of an Arc Computer Vision and Image Understanding, vol. 62, no. 3, pp. 269-278, 1995.
[13] D. Dori, Vector-Based Arc Segmentation in the Machine Drawing Understanding System Environment IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 11, pp. 1057-1068, Nov. 1995.
[14] D. Dori and W. Liu, Stepwise Recovery of Arc Segmentation in Complex Line Environments Int'l J. Document Analysis and Recognition, vol. 1, no. 1, pp. 62-71, 1998.
[15] W. Liu and D. Dori, Incremental Arc Segmentation Algorithm and Its Evaluation IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 4, pp. 424-431, Apr. 1998.
[16] Y. Wei, Circle Detection Using Improved Dynamic Generalized Hough Transform (IDGHT) Proc. IEEE Int'l Symp. Geoscience and Remote Sensing, vol. 2, pp. 1190-1192, 1998.
[17] Y. Wei, A Fast Finding and Fitting Algorithm to Detect Circles Proc. IEEE Int'l Symp. Geoscience and Remote Sensing, vol. 2, pp. 1187-1189, 1998.
[18] P. Dosch, G. Masini, and K. Tombre, Improving Arc Detection in Graphics Recognition Proc. 15th Int'l Conf. Pattern Recognition (ICPR '00), vol. 2, pp. 243-246, 2000.
[19] J. Song, F. Su, C.-L. Tai, J. Chen, and S. Cai, Line Net Global Vectorization: an Algorithm and Its Performance Evaluation Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition (CVPR '00), vol. 1, pp. 383-388, June 2000.
[20] D. Elliman, TIF2VEC, An Algorithm for Arc Segmentation in Engineering Drawings Graphics Recognition Algorithms and Applications, D. Blostein and Y.-B. Kwon, eds., vol. 2390, Lecture Notes in Computer Science, Berlin, Germany: Springer-Verlag, pp. 350-358, 2002.
[21] X. Hilaire, RANVEC and the Arc Segmentation Contest Graphics Recognition Algorithms and Applications, D. Blostein and Y.-B. Kwon, eds., vol. 2390, Lecture Notes in Computer Science, Berlin, Germany: Springer-Verlag, pp. 359-364, 2002.
[22] J. Song, F. Su, C.-L. Tai, J. Chen, and S. Cai, An Object-Oriented Progressive-Simplification Based Vectorization System for Engineering Drawings: Model, Algorithm and Performance IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1048-1060, Aug. 2002.
[23] http://www.cs.cityu.edu.hk/~liuwy/ArcContest ArcSegCon test.htm, 2002.
[24] W. Liu, J. Zhai, and D. Dori, Extended Summary of the Arc Segmentation Contest Graphics Recognition Algorithms and Applications, D. Blostein and Y.-B. Kwon, eds., vol. 2390, Lecture Notes in Computer Science, Berlin, Germany: Springer-Verlag, pp. 343-349, 2002.
[25] R.D.T. Janssen and A.M. Vossepoel, Adaptive Vectorization of Line Drawing Images Computer Vision and Image Understanding, vol. 65, no. 1, pp. 38-56, 1997.
[26] O. Hori and S. Tanigawa, Rastor-to-Vector Conversion by Line Fitting Based on Contours and Skeletons Proc. Int'l Conf. Document Analysis and Recognition (ICDAR '93), pp. 272-281, 1993.
[27] J.B. Roseborough and H. Murase, Partial Eigenvalue Decomposition for Large Image Sets Using Run-Length Encoding Pattern Recognition, vol. 28, no. 3, pp. 421-430, 1995.
[28] D. Dori and W. Liu, Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 3, pp. 202-215, Mar. 1999.
[29] J.Y. Chiang, S.C. Tue, and Y.C. Leu et al., A New Algorithm for Line Image Vectorization Pattern Recognition, vol. 31, no. 10, pp. 1541-1549, 1998.
[30] J.D. Foley, A. van Dam, S.K. Feiner, and J.F. Hughes, Computer Graphics: Principles and Practice. Reading, Mass.: Addison-Wesley, 1990.
[31] http://www.cvc.uab.es/grec2003contest.htm , 2004.
[32] W. Liu, J. Zhai, D. Dori, and L. Tang, A System for Performance Evaluation of Arc Segmentation Algorithms Proc. Third CVPR Workshop Empirical Evaluation Methods in Computer Vision, http://www.cs.cityu.edu.hk/liuwy/ArcContest NoiseModels.pdf, 2001.
[33] http://www.iapr-tc10.or.kr/filecontest99.htm , 2004.
[34] I. Phillips and A. Chhabra, “Empirical Performance Evaluation of Graphics Recognition Systems,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 849-870, Sept. 1999.
[35] A.K. Chhabra and I.T. Phillips, A Benchmark for Graphics Recognition Systems Proc. IEEE Computer Soc. Workshop Empirical Evaluation Methods in Computer Vision, pp. 28-38, June 1998.
[36] A.K. Chhabra and I.T. Phillips, Performance Evaluation of Line Drawing Recognition Systems Proc. 15th Int'l Conf. Pattern Recognition, vol. 4, pp. 864-869, Sept. 2000.

Index Terms:
Graphics recognition, arc segmentation, multiresolution, circular tracking, vectorization, performance evaluation.
Citation:
Jiqiang Song, Michael R. Lyu, Shijie Cai, "Effective Multiresolution Arc Segmentation: Algorithms and Performance Evaluation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1491-1506, Nov. 2004, doi:10.1109/TPAMI.2004.103
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