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[1] Ayache, N. and Faugeras, O. D. HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects, IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1), pp. 44 54, 1986. [2] Ballard, D. H. and Brown, C. M. Computer Vision, Prentice Hall, 1982. [3] Grimson, W. E. L. and Lozano-Perez, T. Localizing overlapping parts by searching the interpretation tree, IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, pp. 469 482, 1987.
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[4] Hummel, R. A. and Zucker, S. W. On the foundation of relaxation labeling processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(3), pp. 267 286, 1983. [5] Li, S. Z. Matching: invariant to translations, rotations and scale changes, Pattern Recognition, 25, pp. 583 594, 1992. [6] Lowe, D. G. Three-Dimensional Object Recognition from Single Two-Dimensional Images, Artificial Intelligence, 31, pp. 355 395, 1987. [7] Etemadi, A., Schmidt, J. P., Matas, G., Illingworth, J. and Kittler, J. Low-level grouping of straight line segments, in Proceedings of 1991 British Machine Vision Conference, pp. 118 126, 1991. [8] Fischler, M. and Elschlager, R. The representation and matching of pictorial structures, IEEE Transactions on Computers, C-22, pp. 67 92, 1973. [9] Grimson, W. E. L. and Huttenlocher, D. On the Sensitivity of the Hough Transform for Object Recognition, Proceedings of the Second International Conference on Computer Vision, pp. 700 706, 1988. [10] Li, S. Z. Markov Random Field Modeling in Computer Vision, Springer Verlag, New York, 1995. [11] Bunke, H. and Buhler, U. Applications of Approximate String Matching to 2D Shape Recognition, Pattern Recognition, 26, pp. 1797 1812, 1993. [12] Cox, I. J., Higorani, S. L. and Rao, S. B. A Maximum Likelihood Stereo Algorithm, Computer Vision and Image Understanding, 63(3), pp. 542 567, 1996. [13] Ohta, Y. and Kanade, T. Stereo by intra- and inter- scanline search using dynamic programming, IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(2), pp. 139 154, 1985. [14] Jacobs, D. W. Robust and Efficient Detection of Convex Groups, IEEE Conference on Computer Vision and Pattern Recognition, pp. 770 771, 1993. [15] Henikoff, J. and Shapiro, L. G. Representative Patterns for Model-based Matching, Pattern Recognition, 26, pp. 1087 1098, 1993. [16] Amini, A. A., Weymouth, T. E. and Jain, R. C. Using dynamic programming for solving variational problems in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(9), pp. 855 867, 1990. [17] Haralick, Y. S. and Shapiro, R. M. Error Propagation in Machine Vision, Machine Vision and Applications, 7, pp. 93 114, 1994. [18] Roh, K. S. and Kweon, I. S. 2-D object recognition using invariant contour descriptor and projective invariant, Pattern Recognition, 31(4), pp. 441 455, 1998. [19] Lee, J. W. and Kweon, I. S. Extraction of Line Features in a Noisy Image, Pattern Recognition, 30(10), pp. 1651 1660, 1997.
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Holo-extraction and Intelligent Recognition of Digital Curves Scanned from Paper Drawings
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Ke-Zhang Chen
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Xi-Wen Zhang
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Laboratory of Human Computer Interaction and Intelligent Information Processing, Institute of Software, the Chinese Academy of Sciences, Beijing 100080, China
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This chapter introduces a holo-extraction method of information from paper drawings, i.e. the networks of Single Closed Regions (SCRs) of black pixels, which not only provide a unified base for recognizing both annotations and the outlines of projections of parts, but can also build the holo-relationships among SCRs so that it is convenient to extract lexical, syntactic and semantic information in the subsequent phases for 3D reconstruction. Based on the holo-extraction method, this chapter further introduces an intelligent recognition method of digital curves scanned from paper drawings for subsequent pattern recognition and 3D reconstruction.
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1. Introduction
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Computer-Aided Intelligent Recognition Techniques and Applications 2005 John Wiley & Sons, Ltd Edited by M. Sarfraz
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