By Topic

Complex nonlinear exponential autoregressive model for shape recognition using neural networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xiong Shenshu ; Dept. of Precision Instrum., Tsinghua Univ., Beijing, China ; Zhou Zhaoying ; Zhong Limin ; Cui Tianhong

A complex nonlinear exponential autoregressive (CNEAR) process which models the boundary coordinate sequence for invariant feature extraction to recognize arbitrary shapes on a plane is presented. All the CNEAR coefficients can be synchronically calculated by using a neural network which is simple in structure and, therefore, easy in implementation. The coefficients are adopted to constitute the feature set which are proven to be invariant to the transformation of a boundary such as translation, rotation, scale and choice of the starting point in tracing the boundary. Afterwards, the feature set is used as the input to a complex multilayer perceptron (C-MLP) network for learning and classification. Experimental results show that complicated shapes can be recognized in high accuracy, even in the low-order model. It is also seen that the classification method has a good degree of fault tolerance when noise is present

Published in:

Instrumentation and Measurement Technology Conference, 1998. IMTC/98. Conference Proceedings. IEEE  (Volume:1 )

Date of Conference:

18-21 May 1998