Much of our understanding of the relationships among geometric struc-
tures in images is based on the shape of these structures and their
relative orientations, positions and sizes. Thus, developing
quantitative methods for capturing shape information from digital images
is an important area for computer vision research. This book describes
the theory, implemen- tation, and application of two multi resolution
image shape description methods. The author begins by motivating the
need for quantitative methods for describing both the spatial and
intensity variations of struc- tures in grey-scale images. Two new
methods which capture this informa- tion are then developed. The first,
the intensity axis of symmetry, is a collection of branching and bending
surfaces which correspond to the skeleton of the image. The second
method, multiresolution vertex curves, focuses on surface curvature
properties as the image is blurred by a sequence of Gaussian filters.
Implementation techniques for these image shape descriptions are
described in detail. Surface functionals are mini- mized subject to
symmetry constraints to obtain the intensity axis of symmetry. Robust
numerical methods are developed for calculating and following vertex
curves through scale space. Finally, the author demon- strates how
grey-scale images can be segmented into geometrically coher- ent regions
using these shape description techniques. Building quantita- tive
analysis applications in terms of these visually sensible image regions
promises to be an exciting area of biomedical computer vision research.
v Acknowledgments This book is a corrected and revised version of the
author's Ph. D.