This text covers Gaussian scale-space theory from its applications to its mathematical foundation. The reader not so familiar with scale-space should find it instructive to first consider some potential applications described in Part I. The next two parts both address fundamental aspects of scale-space. Whereas scale is treated as an essentially arbitrary constant in Part II, Part III emphasises the deep structure, i.e. the structure that is revealed by varying scale. Finally Part IV is devoted to non-linear extensions, notably non-linear diffusion techniques and morphological scale-spaces, and their relation to the linear case. The book is addressed to researchers in the field of image analysis seeking mathematical foundation of algorithms.
