An algorithm for line matching in an image by mapping into an n-dimensional vector space

Abstract

This paper proposes a minimal length difference algorithm for construction of a line in an image by solvingthe problem of optimal contour approximation. In this algorithm, a method for finding interest points is proposed, andthe object matching (classification) is done by mapping interest points onto a vector space. In cases where the lines inthe representation of the images are not smooth, the algorithm converges rapidly. The results of the experiments showedthat for convergence of the contour simplification, there were 5-6 iterations for n = 13. To check how close the curveapproximation calculated by the algorithm above, the researchers have calculated the length of the curve simplificationmanually. This length was then compared to the length of the original curve. The results showed that the length of thesimplified curve grew rapidly to 92%–95% of the original curve length. The further increase in the number of points doesnot affect this indicator. According to the obtained results, the relative difference and the relative difference distanceare good metrics to match objects.