Optimal Scanning of Gaussian and Fractal Brownian Images with an Estimation of Correlation Dimension

The paper considers the influence of the image pixels position in a one-dimensional sequence on the result of correlation dimension evaluation. The sequence formed as a result of reading of the two-dimensional pixel image. Dimension evaluation is performed by maximum likelihood method, using image elements ordering and creating vectors in pseudophase space by Takens theorem, and is used as a texture feature if texture processing and detection of objects. The two different scanning methods are considered. There is a problem of optimization of the scan path in order to maintain correlations between pixels in sequence. The first method is to scan on the criterion of maximum correlation between the adjacent sets of pixels. The second method deals with choice of scan direction by criterion of scalar product maximum of chosen vector and previous one. An estimator of correlation dimension is evaluated.