1992
@incollection{Wei1992,
vgclass = {refpap},
vgproject = {invariance},
author = {Issac Weiss},
title = {Noise-resistant invariants of curves},
editor = {Joseph Mundy and Andrew Zisserman},
booktitle = {Geometric Invariance in Computer Vision},
address = {Cambridge, MA, USA},
series = {Series: Artificial intelligence},
pages = {135--156},
publisher = {The MIT Press},
year = {1992},
abstract = {In this chapter we concentrate on local or differential
invariants of curves. Unlike global (algebraic) invariants, they do not
suffer from the occlusion problem. A representation of a general curve
is obtained which is independent of the viewpoint from which the curve
is seen. This ``signature'' can be used to match an observed shape to
one stored in a database, without having to search for the correct
viewpoint. Both projective and affine invariants are treated. The
reliability is improved in two ways: (1) We have found new methods for
obtaining differential invariants which are more robust. (2) We have
developed differentiation techniques which give much more reliable
results than previous ones. These differentiation methods are useful in
many other applications as well.},
}