Optimum polygonal approximation of digitized curves
Pattern Recognition Letters
Techniques for Assessing Polygonal Approximations of Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Trajectory Segmentation Using Dynamic Programming
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Reduced-search dynamic programming for approximation of polygonal curves
Pattern Recognition Letters
Approximation of digital curves with line segments and circular arcs using genetic algorithms
Pattern Recognition Letters
Online Segmentation of Freehand Stroke by Dynamic Programming
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Approximation of a polyline with a sequence of geometric primitives
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
An optimal polygonal boundary encoding scheme in the rate distortion sense
IEEE Transactions on Image Processing
Segmentation and multi-model approximation of digital curves
Pattern Recognition Letters
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In this paper we have examined a problem of piecewise approximation of digital curves with a set of models. Each segment of the input curve was approximated by a function selected from a given set of functions (line segments, circular arcs, polynomials, splines, etc). Following the Minimum Description Length principle, we have introduced a fast near-optimal algorithm for multi-model error-bounded approximation of digital curves. The algorithm was tested on a large-sized test data se and demonstrated a sufficient tradeoff between time performance and efficiency of solutions. The processing time for the large-size test data is less than 1s.