الفهرس 
Acknowledgement
chapter 1Only 14 pages are availabe for public view
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Abstract
Artificial Neural Networks have broad applications to the real world
business problems. They have already been successfully applied in many
industries. Since neural networks are best at identifying patterns or trends in
data, they are well suited for prediction or forecasting. These include Sales
forecasting, Industrial process control, Customer research, Data validation, Risk
management, Target marketing. The thesis studies the use of Artificial Neural
Network in the field of Image Processing. One of the applications studied is the
edge detection process. Edge detection of an image significantly reduces the
amount of data and filters out useless information, while preserving the
important structural properties in an image. Edges detection of digital images is
used in a various fields of applications ranging from real-time video
surveillance and traffic management to medical imaging applications. Thesis
demonstrates both entropy and Neural Network based edge detection methods,
where Renyi’s Entropy and Convolutional Neural Network based edge detection is proposed and their results are compared. Thesis presents another
application of Artificial Neural Network, An identification system using eye
detection based on Wavelets and Neural Networks, for recognizing humans
using their eyes as a biometric identity. The system is first trained to learn how
to identify each eye and generate a unique identity for each eye, and then the
person is recognized when his eye identity matches the previously defined one.
The identification system proposed is efficient and robust against different
image conditions.
Thesis consists mainly of six chapters as follows: Chapter one presents the motivation behind this thesis, and defined the
fundamental problem considered with real applications. It also summarized the
contributions made toward this problem.
Chapter two contains general concepts about Artificial Neural Network and its
history, architecture and applications.
Chapter three discusses a new proposed Renyi’s entropy algorithm for edge
detection in level images and presenting the algorithm and simulation results
Chapter four presents a new technique including the Implementation of
Convolutional Neural Network, applying it for edge detection, experiment
discussion, simulation results and a full comparison between Renyi’s Entropy
edge detection and Convolutional Neural Network based edge detection.
Chapter five studies Biometric Technology, Introduces eye as a perfect ID, how
to use discrete Wavelet transform and Artificial Neural Network to eye
extraction and recognition then discusses proposed identification system, finally
the experiment discussion, performance and simulation results.
In chapter six we present the summary, conclusion of our thesis and future
work.