1992
@incollection{MuZ1992a,
vgclass = {refpap},
vgproject = {invariance},
author = {Joseph L. Mundy and Andrew Zisserman},
title = {Towards a New Framework for Vision},
editor = {Joseph Mundy and Andrew Zisserman},
booktitle = {Geometric Invariance in Computer Vision},
address = {Cambridge, MA, USA},
series = {Series: Artificial intelligence},
pages = {1--39},
publisher = {The MIT Press},
year = {1992},
abstract = {The importance of invariance to machine vision has been
recognized since the origin of the field in the 1960s. Initially, the
emphasis was on invariance to \emph{photometric} properties and this
provided the impetus for the development of edge detectors. In this
book we are concerned with \emph{geometric} invariance.
Invariants are properties of geometric configurations which remain
unchanged under an appropriate class of transformations. In many case the
\emph{only} properties of a configuration that are of any interest are
those that are invariant under a particular set of transformations. The
straight line is the basic feature used in vision systems precisely because
it is the simplest projective invariant - an imaged straight line is
straight.
In this overview we outline the applications of geometric invariants in
machine vision and provide a brief review of the principles of invariance.
The remainder of the introduction examines these issues in more detail and
provides a guide to current and future applications of geometric invariance
in machine vision.},
}