|
In this paper, a reliable and efficient pattern recognition
system is developed to find all the conjugate relations between
two groups of features. This system conceptually mime the human
recognition process.It is expected to be adaptive to
disturbances of distortion, noises and differences of
orientation between two conjugate features. The system is
achieved through the considerations of some quantitative
information derived based on the similarity of shape and
consistency of orientation. An optimal conjugate relation
between two feature groups can be determined after taking all
of the quantitative information into consideration. In order to
obtain quantified recognition information,features are
described by using Fourier descriptors. Then, the shape
similarity and the orientation differences are calculated by
using the least-squares approach to matching Fourier
descriptors. The Hopfield-Tank neural network is used to
combine all information and determine the optimal conjugate
relation. The neural network simulates the operation of human
neuron so as to achieve the flexibility and adaptation of human
brain. In the network, the conjugate relations between two
feature groups are searched based on an energy value which is a
function of the shape similiarity, the orientation consistency,
constraints of conjugate relations. The optimal state of the
conjugate relations between two groups of features is reached
when the minimum energy value is obtained. Features fetched
from automatic image segmentation process of two conjugate
aerial images and digitized from conjugate aerial images are
took as the experiment data to test the feasibility of this
recognition theory. The test shows an encouraging result. end.
|