A novel split-and-merge technique for error-bounded polygonal approximation

Authors:
Bin Wang;Chaojian Shi
Affiliations:
Department of Computer Science and Engineering, Fudan University, Shanghai, P.R. China;Department of Computer Science and Engineering, Fudan University, Shanghai, P.R. China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 4
Cited 0

On the Detection of Dominant Points on Digital Curves

IEEE Transactions on Pattern Analysis and Machine Intelligence
An algorithm for polygonal approximation based on iterative point elimination

Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Adaptive Selection Methods for Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms

Quantified Score

Hi-index	0.00

Visualization

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.