Application of 2D Fourier Descriptors and Similarity Measures to the General Shape Analysis Problem
The General Shape Analysis (GSA) is a problem of finding the most similar basic shape to the test one. It is close to traditional recognition or retrieval of shapes. Main difference is that GSA does not aim at the identification of an exact object shape but at the indication of one or few most similar to it general templates – simple shape figures, e.g. rectangle, circle or triangle. By comparing more complicated shapes with simple ones it is possible to determine the most general information about a particular object. In order to perform the comparison using the template matching approach it is necessary to define methods for the representation and similarity estimation of shapes. In this paper the attention is paid to two-dimensional Fourier Descriptor applied for the representation of a shape and two matching methods, namely Euclidean distance and correlation. The effectiveness of the shape descriptor is estimated as a convergence between the experimental results and results provided by humans through the inquiry forms concerning the same GSA task. Performed experiments allowed us to determine the influence of the matching method on the final effectiveness of the approach applying Fourier Descriptors. Selection of the absolute spectrum subpart size is also discussed.
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