Abstract
How to use a polygon with the fewest possible sides to approximate a shape boundary is an important issue in pattern recognition and image processing. A novel split-and-merge technique(SMT) is proposed. SMT starts with an initial shape boundary segmentation, split and merge are then alternately done against the shape boundary. The procedure is halted when the pre-specified iteration number is achieved. For increasing stability of SMT and improving its robustness to the initial segmentation, a ranking-selection scheme is utilized to choose the splitting and merging points. The experimental results show its superiority.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, B., Shi, C. (2006). A Novel Split-and-Merge Technique for Error-Bounded Polygonal Approximation. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_37
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DOI: https://doi.org/10.1007/11893257_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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