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