Research On The Rotation Invariant Texture Characterization And Classification

Texture analysis as a basic issue in computer vision and image processing has made great progress during the past few years. Now more and more attention is being paid to the invariant texture analysis, especially to the rotation invariant texture analysis. Texture invariant analysis has played a very important role in many fields, such as: content based image retrieval,remote sensing image analysis,medical image analysis,Biometrics and target recognition and other fields. Although a great progress has been made during the past few decades, the precision and efficiency of the identification are still not meet the requirements of the practical needs. So a further research is still needed.In this paper, there is a comprehensive introduction of the texture, which gives the definition of texture from different views, specify the current work and introduce several analysis algorithms on texture analysis in four categories: statistical methods, model based methods, and structural methods and frequency/time methods; Then two methods for the rotation invariant texture analysis are present and experimented. The main work of this thesis is summarized as follows:1. A new rotation-invariant texture analysis technique using Hough and Fourier transforms is proposed, in which the Hough transform of the image is first calculated and then the Fourier transforms and its corresponding module is computed to extract the invariant features. In the Hough transform process, the rotation quantity in the rotated image is transformed into the motion quantity in the image; we calculate the Hough transform for a disk area of the texture image; then after using a Fourier transform and following making a module, as the module has the property of translation invariant, so the translation quantity is also eliminated and the description of rotation invariant features of texture image are gained. The theoretical analysis and experimental results also show the feasibility and good robustness of this method.2. In a review of the most popular mathematical tool of the wavelet, a rotation-invariant texture analysis method based on the polar transform and adaptive row shift invariant wavelet packet transform is also present. The polar transform converts a given image into a rotation-invariant but row-shifted image, which is then passed to the adaptive row shift invariant wavelet packet transform to generate adaptively some subbands of rotation-invariant wavelet coefficients. An energy signature is computed for each subband of these wavelet coefficients. Some of the most dominant polar-wavelet energy signatures are selected as the rotation invariant texture features. Experimental results show that this rotation-invariant texture feature is effective and robust to the noise.In a word, texture invariant analysis is one of the challenging topics in texture analysis, and rotation-invariant analysis is one of the difficult issues. A new rotation invariant texture analysis technique by Hough and Fourier transform is proposed in this paper, the theoretic analysis and related experiments have proved that this method has good classification efficiency and robustness. Those works in this paper are hoped to do some promotion action with the future researches about the texture invariant analysis.