Muzzle
Classification Using Neural Networks
Ibrahim El-Henawy1, Hazem El-bakry2,
and Hagar El-Hadad3
1Department of Information Systems, Zagazig
University, Egypt
2Department of Information Systems, Mansoura
University, Egypt
3Department of Information Systems, Beni-Suef
University, Egypt.
Abstract: There
are multiple techniques used in image classification such as Support Vector
Machines (SVM), Artificial Neural Networks (ANN), Genetic Algorithms (GA),
Fuzzy measures, and Fuzzy Support Vector Machines (FSVM). Classification of
muzzle depending on one of this artificial technique has become widely known
for guaranteeing the safety of cattle products and assisting in veterinary
disease supervision and control. The aim of this paper is to focus on using
neural network technique for image classification. First the area of interest
in the captured image of muzzle is detected then pre-processing operations such
as histogram equalization and morphological filtering have been used for
increasing the contrast and removing noise of the image. Then, using box-counting
algorithm to extract the texture feature of each muzzle. This feature is used
for learning and testing stage of the neural network for muzzle classification.
The experimental result shows that after 15 input cases for each image in
neural training step, the testing result is true and gives us the correct
muzzle detection. Therefore, neural networks can be applied in classification
of bovines for breeding and marketing systems registration.
Keywords: Muzzle
classification, image processing, neural networks.
Received
November 7, 2014; accepted February 5, 2015