Multi-texture image segmentation
Abstract
Visual perception of images is closely related to the recognition of the different
texture areas within an image. Identifying the boundaries of these regions is an important
step in image analysis and image understanding. This thesis presents supervised and
unsupervised methods which allow an efficient segmentation of the texture regions within
multi-texture images.
The features used by the methods are based on a measure of the fractal dimension
of surfaces in several directions, which allows the transformation of the image into a set
of feature images, however no direct measurement of the fractal dimension is made. Using
this set of features, supervised and unsupervised, statistical processing schemes are
presented which produce low classification error rates. Natural texture images are
examined with particular application to the analysis of sonar images of the seabed.
A number of processes based on fractal models for texture synthesis are also
presented. These are used to produce realistic images of natural textures, again with
particular reference to sonar images of the seabed, and which show the importance of
phase and directionality in our perception of texture. A further extension is shown to give
possible uses for image coding and object identification.