Wavelet transform in image recognition

Texture segmentation and classification form a very important topic of the interdisciplinary area of signal processing with many applications in different areas including satellite image processing, biomedical image analysis and microscopic image processing. The paper presents selected mathematical methods used for image segmentation and the following segments classification using multiresolution decomposition of segments boundary signals. The wavelet transform has been applied here for feature extraction and image de-noising. Results of feature extraction obtained by the discrete wavelet transform are compared with that evaluated by the discrete Fourier transform. For the following feature classification the self-organizing neural networks are applied. Proposed methods have been verified for simulated structures and then used for analysis of microscopic images of crystals of different shapes and sizes.