Minimum Description Length approximation of digital curves

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 trade-off between time performance and efficiency of solutions. The processing time for the large-size test data is less than 1s.