Abstract
Curve matching is an important problem in pattern recognition with a variety of applications including model based recognition. In these applications the two matched curves are usually very similar. An application of curve matching to model based recognition involves typically a decision whether a model curve and an image curve are the same, up to some scaling or a transformation and some permitted level of noise. Most of the work on curve matching relates directly to contour matching, where one should further make a distinction between dense matching and feature matching. The latter approach is based on a set of features, calculated for both contours. In that case, the distance of the contours in feature space is used as matching criterion. Dense matching is usually formulated as a parameterisation problem, with some cost function to be minimised. The cost might be defined as the elastic energy needed to transform one curve to the other [1, 2, 3], but other alternatives exist [4, 5, 6].
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Nyssen, E., Truyen, B., Sahli, H. (2003). Energy Minimisation Methods for Static and Dynamic Curve Matching. In: Chen, D., Cheng, X. (eds) Pattern Recognition and String Matching. Combinatorial Optimization, vol 13. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0231-5_22
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DOI: https://doi.org/10.1007/978-1-4613-0231-5_22
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