Automatic iection of products and plants plays a significant role in industries. Steel industry is one of the big industries that in different stages of its production line needs iection. Mobarakeh steel company is the biggest steel maker company in Iran. Iection of different units of this company (e.g. pelletizing plant) has been done by human, such that the quality is enhanced and the probabilistic costs are decreased. In the peletizing unit of Mobarakeh steel company, 260 pallets exist. Pellets are posed on the pallets with 150 cm width and 360 cm length. The pellets undergo drying and preheating in the stove. There are four rows of grate-bars on the surface of the pallet. Each row consists of 90 grate-bars. Gradually the grate-bars can be damaged because of the high temperature of stove, sudden change of temperature and hitting the pellets to the grate-bars. The damages cause spaces between grate-bars which lead to losses in pelletizing unit. If the damages periodically are observed and reported then, these losses can be prevented through replacing the pallets. For this reason, pallets need permanently to be iected. Traditionaly, this iection has been done by human. In this thesis, a method for automatic detection of these damages is presented. In the first step, the pallet area is segmented from the image by an example-based learning approach where a model of an object align=left Keywords: Automatic iection, image segmentation, image enhancement, feature extraction, support vector machine ltr"
