The analysis of image characteristics on the base of energy features of the wavelet transform

Abstract

The article shows the relevance of creating models and methods that provide effective solutions to image processing and analyzing problems in computer vision systems. We consider models of an average level of image representation. They are constructed on the basis of their characteristic features (contours, regions and points of interest). To construct such models, we suggest using the procedure of forming energy features based on the wavelet transform. As a result, the original image will be transformed to a view where different points will have different weights. That characterize their contribution to the overall energy of the image. It is also possible to provide a selection of tuning coefficients. It allows to take into account the interrelations between the wavelet coefficients of various scales. After receiving the weight images, they can be processed to form the required characteristics. For example, to obtain contours, you can perform binarization of a weight image with a certain threshold. To get singular points, you can define a specified number of the most significant weights in different areas of the weight image. For texture analysis, you can use statistical characteristics calculated by the histogram of the scale.

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