Search results for key=SqC2000 : 1 match found.

2000

• @article{SqC2000,
  	vgclass =	{refpap},
  	vgproject =	{nn,invariance},
  	author =	{David McG. Squire and Terry M. Caelli},
  	title =	{Invariance Signatures: Characterizing contours by their
  	departures from invariance},
  	journal =	{Computer Vision and Image Understanding},
  	volume =	{77},
  	number =	{3},
  	pages =	{284--316},
  	month =	{March},
  	year =	{2000},
  	doi =	{https://doi.org/10.1006/cviu.2000.0809},
  	url =	{/publications/postscript/1999/SquireCaelli_cviu99.pdf},
  	url1 =	{/publications/postscript/1999/SquireCaelli_cviu99.ps.gz},
  	abstract =	{In this paper, a new invariant feature of two-dimensional
  	contours is reported: the Invariance Signature. The Invariance
  	Signature is a measure of the degree to which a contour is invariant
  	under a variety of transformations, derived from the theory of Lie
  	transformation groups. It is shown that the Invariance Signature is
  	itself invariant under shift, rotation and scaling of the contour.
  	Since it is derived from local properties of the contour, it is
  	well-suited to a neural network implementation. It is shown that a
  	Model-Based Neural Network (MBNN) can be constructed which computes the
  	Invariance Signature of a contour, and classifies patterns on this
  	basis.  Experiments demonstrate that Invariance Signature networks can
  	be employed successfully for shift-, rotation- and scale-invariant
  	optical character recognition.},
  }