TY - JFULL
AU - Hazem M. El-Bakry and Qiangfu Zhao
PY - 2007/12/
TI - A Modified Cross Correlation in the Frequency Domain for Fast Pattern Detection Using Neural Networks
T2 - International Journal of Computer, Electrical, Automation, Control and Information Engineering
SP - 3753
EP - 3760
EM - helbakry20@yahoo.com, qf-zhao@u-aizu.ac.jp
VL - 1
SN - 1307-6892
UR - http://waset.org/publications/2525
PU - World Academy of Science, Engineering and Technology
NX - International Science Index 11, 2007
N2 - Recently, neural networks have shown good
results for detection of a certain pattern in a given image. In
our previous papers [1-5], a fast algorithm for pattern
detection using neural networks was presented. Such
algorithm was designed based on cross correlation in the
frequency domain between the input image and the weights
of neural networks. Image conversion into symmetric shape
was established so that fast neural networks can give the
same results as conventional neural networks. Another
configuration of symmetry was suggested in [3,4] to improve
the speed up ratio. In this paper, our previous algorithm for
fast neural networks is developed. The frequency domain
cross correlation is modified in order to compensate for the
symmetric condition which is required by the input image.
Two new ideas are introduced to modify the cross correlation
algorithm. Both methods accelerate the speed of the fast
neural networks as there is no need for converting the input
image into symmetric one as previous. Theoretical and
practical results show that both approaches provide faster
speed up ratio than the previous algorithm.
ER -