Computational-geometric methods for polygonal approximations of a curve
Computer Vision, Graphics, and Image Processing
Fitting polygonal functions to a set of points in the plane
CVGIP: Graphical Models and Image Processing
Efficient piecewise-linear function approximation using the uniform metric: (preliminary version)
SCG '94 Proceedings of the tenth annual symposium on Computational geometry
On approximating polygonal curves in two and three dimensions
CVGIP: Graphical Models and Image Processing
Approximating monotone polygonal curves using the uniform metric
Proceedings of the twelfth annual symposium on Computational geometry
Efficiently approximating polygonal paths in three and higher dimensions
Proceedings of the fourteenth annual symposium on Computational geometry
Plane Sweep Algorithms for the Polygonal Approximation Problems with Applications
ISAAC '93 Proceedings of the 4th International Symposium on Algorithms and Computation
Parallel Algorithms for Partitioning Sorted Sets and Related Problems
ESA '96 Proceedings of the Fourth Annual European Symposium on Algorithms
An extraction technique of optimal interest points for shape-based image classification
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Of motifs and goals: mining trajectory data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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Given an n-vertexp olygonal curve P = [p1, p2,..., pn] in the 2-dimensional space R2, we consider the problem of approximating P by finding another polygonal curve P′ = [p′1, p′2,..., p′m] of m vertices in R2 such that the vertexs equence of P′ is an ordered subsequence of the vertices along P. The goal is to either minimize the size m of P′ for a given error tolerance ∈ (called the min-# problem), or minimize the deviation error ∈ between P and P′ for a given size m of P′ (called the min-∈ problem). We present useful techniques and develop a number of efficient algorithms for solving the 2-D min-# and min-∈ problems under two commonly-used error criteria for curve approximations. Our algorithms improve substantially the space bounds of the previously best known results on the same problems while maintain the same time bounds as those of the best known algorithms.