Techniques for the analysis of texture in digital images are essential to a range of applications in areas as diverse as robotics, defence, medicine and the geo-sciences. In biological vision, texture is an important cue allowing humans to discriminate objects. This is because the brain is able to decipher important variations in data at scales smaller than those of the viewed objects. In order to deal with texture in digital data, many techniques have been developed by image processing researchers.

　　1.provides detailed descriptions of methods used to analyse binary as well as grey texture images

　　2.presents information on two levels: an easy-to-follow narrative explaining the basics, and an advanced, in-depth study of mathematical theorems and concepts

　　3.looks at ‘good’ and ‘bad’ image processing practice, with wrongly designed algorithms illustrating ‘what not to do’

　　4.includes accompanying website, setting out all algorithms discussed within the text.

Preface.

1 Introduction.

2 Binary textures.

2.1 Shape grammars.

2.2 Boolean models.

2.3 Mathematical morphology.

3 Stationary grey texture images 81

3.1 Image binarisation.

3.2 Grey scale mathematical morphology.

3.3 Fractals.

3.4 Markov random fields.

3.5 Gibbs distributions.

3.6 The autocorrelation function as a texture descriptor.

3.7 Texture features from the Fourier transform.

3.8 Co-occurrence matrices.

4 Non-stationary grey texture images.

4.1 The uncertainty principle and its implications in signal and image processing.

4.2 Gabor functions.

4.3 Prolate spheroidal sequence functions.

4.4 Wavelets.

4.5 Where Image Processing and Pattern Recognition meet.

4.6 Laws’ masks and the ‘‘what looks like where’’ space.

4.7 Local binary patterns.

4.8 The Wigner distribution.

Bibliographical notes.

References.

Index.