Complex autoregressive model for shape recognition
Sekita, I.
Kurita, T.
Otsu, N.
Electrotech. Lab., MITI, Ibaraki;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Apr 1992
Volume: 14,
Issue: 4
On page(s): 489-496
ISSN: 0162-8828
References Cited: 20
CODEN: ITPIDJ
INSPEC Accession Number: 4166331
Digital Object Identifier: 10.1109/34.126809
Current Version Published: 2002-08-06
Abstract
A complex autoregressive model for invariant feature extraction to
recognize arbitrary shapes on a plane is presented. A fast algorithm to
calculate complex autoregressive coefficients and complex PARCOR
coefficients of the model is also shown. The coefficients are invariant
to rotation around the origin and to choice of the starting point in
tracing a boundary. It is possible to make them invariant to scale and
translation. Experimental results that the complicated shapes like
nonconvex boundaries can be recognized in high accuracy, even in the
low-order model. It is seen that the complex PARCOR coefficients tend to
provide more accurate classification than the complex AR coefficients
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
