Normalization of 2D Images “in Terms of Level” Based on Cosine and Hadamard Transform
Keywords:normalization in terms of level, image classification, Hadamard transform, cosine transform, pattern recognition
Background. The problem of pattern recognition is considered in this paper. We propose to use orthogonal basis functions for pattern recognition of 2D signals.
Objective. The aim of this paper is investigation of pattern recognition problem of 2D images based on cosine and Hadamard transforms.
Methods. Pre-processing of signals is proposed to reduce the number of spectral components in the orthogonal series of signal decomposition. Normalization of a reference signal “in terms of level” allows comparing reference and tested signals by means of transform coefficient calculation. This coefficient is a match criterion of these signals.
Results. Theoretical information used for proposed classification method is described. The normalization algorithms of the reference and tested signals, which have to perform before determining similarity of images, are proposed. Transformation of a reference signal to two-dimensional basic function is fundamental point. This basic function is based on selected 2D orthogonal transform. Complete cycle of calculation for pattern recognition of some 2D images is executed. The calculations were performed using Hadamard and cosine transforms. 2D images of obtained spectra are given.
Conclusions. As a result, this research shows that we can perform pattern recognition of normalized 2D images based on calculation of transform coefficient. The analysis of calculation results shows that classification of signals based on transform coefficient values is possible. Development of matching classifiers is the aim of the next research.
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