Refereed full papers (journals, book chapters, international conferences)

1993

@book{Rei1993,
	vgclass =	{refpap},
	vgproject =	{invariance},
	author =	{Thomas H. Reiss},
	title =	{Recognizing Planar Objects Using Invariant Image Features},
	number =	{676},
	series =	{Lecture Notes in Computer Science},
	publisher =	{Springer-Verlag},
	address =	{Berlin},
	year =	{1993},
	abstract =	{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 \emph{image invariants}, for planar objects.

	Following the introduction in chapter 1, the book discusses features
	that are invariant to image translations, rotations, to changes in size
	and contrast, with particular attention being paid to the effect of
	using discrete images rather than continuous ones. The next chapter
	presents a tutorial introduction to the theory of algebraic invariants
	which lies at the heart of two important types of invariant features:
	moment invariants for affine transformations, and projective invariants
	for perspective transformations. Chapter 4 is devoted entirely to
	features invariant to affine transformations: the theory behind
	moment-based invariants, Fourier descriptors and differential
	techniques is presented, along with a novel technique based on
	correlations, and results of experiments on the stability of coarsely
	sampled images are discussed. Chapter 5 goes one step further and
	covers features invariant to perspective transformations, summarizing
	work on both differential and global invariants. The penultimate
	chapter, chapter 6, shows how invariant features can be used to
	recognize objects that have been partially occluded. A thorough
	treatment of the `geometric hashing' method is given, followed by some
	novel methods of `back-projection' which allows one to verify whether
	the hypothesized object really is in the image. Many authors claim that
	moment invariants cannot be used under partial occlusion; this is not
	so, and a number of schemes for their use are presented. Not only can
	they be used, but they have some significant advantages over other
	invariant features, a fact that is backed up by experiments. The final
	chapter contains a summary and conclusions.},
}