الفهرس 
AbstractOnly 14 pages are availabe for public view
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Abstract
Ministry of Agriculture (Livestock Sector) and veterinarians search for new artificial techniques to save bovine’s livestock and products. Bovines muzzle images are considered a biometric identifier to guarantee and maintain the safety of bovine’s livestock products. This thesis presents nine different experiments to identify bovines depending on their muzzle images. Each experiment has three different parts. The first experiment depends on two parts before the last part which is the identification part using Artificial Neural Network (ANN). The pre-processing part consists of two phases; histogram equalization (HEQ) and mathematical morphology (MMF) while the feature extraction uses box-counting algorithm. The second experiment is based on using HEQ and MMF in the first part, the second part is based on Segmentation-based Fractal Texture Analysis (SFTA) instead of box-counting algorithm and the last part uses ANN. The third experiment is based on HEQ and MMF in the first part and texture feature extracting part is based on box-counting and SFTA. The fourth based on using HEQ and MMF in the first part, box-counting algorithm in the second part and decision tree in the last part. The fifth experiment replaces the second part in the fourth experiment with SFTA and all phases still as it. The sixth experiment used average filter and median filter in the first part in order to remove noise from muzzle image and save the original pixels of the muzzle image, the second part is based on using Gray level co-occurrence matrix (GLCM) in order to extract muzzle features for each bovine, the last part is based on using Naïve Bayes. The seventh experiment is as the previous experiment. The only difference is in the last phase which is identification part and uses decision tree. The eighth experiment likes the sixth one but the difference in the second part which uses Discrete Wavelet Transform (DWT). The ninth and last experiment is like the seventh one but the second part uses Discrete Wavelet Transform (DWT) in order to extract image texture feature.