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Geometric primitive extraction by the combination of tabu search and subpixel accuracy

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Abstract

In this paper, a novel method for extracting the geometric primitives from geometric data is proposed. Specifically, tabu search is combined with subpixel accuracy to improve detection accuracy and convergent speed. On the one hand, this new shape detection method not only has TS's ability to find the global optimum, but also keeps all advantages of tabu search. On the other hand, it has subpixel accuracy ability to match the local optimum.

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Authors and Affiliations

  1. School of Mathematics, The University of New South Wales, 2052, Sydney, Australia

    Jiang Tianzi

  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100080, Beijing, P.R. China

    Jiang Tianzi

Authors
  1. Jiang Tianzi
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Correspondence to Jiang Tianzi.

Additional information

This work was partially supported by the National Natural Science Foundation of China, and the Vice-Chancellor's Post Doctoral Fellowship of UNSW, Australia.

JIANG Tianzi was born in 1962. He received the B.Sc. degree in computational mathematics from Lanzhou University in 1984, the M.Sc. and Ph.D. degrees in computational mathematics from Hangzhou University in 1992 and 1994, respectively. From 1994 to 1996, he worked as a postdoctoral fellow at National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences. Now he is an Associate Professor at NLPR and a Vice-Chancellor's Post Doctoral Research Fellow at the University of New South Wales in Australia. He is a member of New York Academy of Sciences, IEEE, and IEEE Computer Society. He authored and coauthored more than 40 referred papers on computer vision, pattern recognition, computational complexity and applied mathematics.

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Jiang, T. Geometric primitive extraction by the combination of tabu search and subpixel accuracy. J. Comput. Sci. & Technol. 14, 74–80 (1999). https://doi.org/10.1007/BF02952490

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  • DOI: https://doi.org/10.1007/BF02952490

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