Given a familiar object extracted from its surroundings, we
humans have little difficulty in recognizing it irrespective
of its size, position and orientation in our field of view.
Changes in lighting and the effects of perspective also pose
no problems. How do we achieve this, and more importantly,
how can we get a computer to do this? One very promising
approach is to find mathematical functions of an object's
image, or of an object's 3D description, that are invariant
to the transformations caused by the object's motion.
This book is devoted to the theory and practice of such
invariant image features, so-called image invariants, for
planar objects. It gives a comprehensive summary of the
field, discussing methods for recognizing both occluded and
partially occluded objects, and also contains a definitive
treatmentof moment invariants and a tutorial introduction
to algebraic invariants, which are fundamental to affine
moment invariants and to many projective invariants.
A number of novel invariant functions are presented and the
results of numerous experiments investigating the stability
of new and old invariants are discussed. The main conclusion
is that moment invariants are very effective, both for
partially occluded objects and for recognizing objects in
grey-level images.