Optimum Uniform Piecewise Linear Approximation of Planar Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Detection of Dominant Points on Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting the dominant points by the curvature-based polygonal approximation
CVGIP: Graphical Models and Image Processing
A non-parametric sequential method for polygonal approximation of digital curves
Pattern Recognition Letters
Optimum polygonal approximation of digitized curves
Pattern Recognition Letters
Techniques for Assessing Polygonal Approximations of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on minimal length polygonal approximation to a digitized contour
Communications of the ACM
Digital Image Processing
Pattern Recognition Letters
Piecewise Polygonal Approximation of Digital Curves
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Approximating a set of points by a step function
Journal of Visual Communication and Image Representation
Angle Detection on Digital Curves
IEEE Transactions on Computers
Polygonal approximation of digital planar curves through break point suppression
Pattern Recognition
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Given a closed curve with n points, based on the local integral square error and the curvature constraint criteria, this paper presents a novel two-pass O(Fn+mn^2)-time algorithm for solving the closed polygonal approximation problem where m(@?n) denotes the minimal number of covering feasible segments for one point and empirically the value of m is rather small, and F (@?n^2) denotes the number of feasible approximate segments. Based on some real closed curves, experimental results demonstrate that under the same number of segments used, our proposed two-pass algorithm has better quality and execution-time performance when compared to the previous algorithm by Chung et al. Experimental results also demonstrate that under the same number of segments used, our proposed two-pass algorithm has better quality, but has some execution-time degradation when compared to the currently published algorithms by Wu and Sarfraz et al.