نویسندگان
1 مهندسی پلیمر، دانشگاه صنعتی امیرکبیر
2 دانشکده مهندسی پلیمر و رنگ، دانشگاه صنعتی امیر کبیر
3 دانشکده مهندسی پلیمر و رنگ، دانشگاه صنعتی امیرکبیر
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Consumers increased expectations of high quality food products as well as stringent regulations has increased the need for an accurate and fast method for quality assessment and control of the products in food industries. Machine vision with the aid of various image processing methods has been introduced as an objective, automate, and non-destructive approach capable for food quality control. Texture as one of the most important images features has been used extensively in food quality monitoring applications. Generally, quantitative texture assessment methods are divided into four groups: statistical, structural, model-based, and transform-based methods. In the first part of this research (part A), the statistical methods are reviewed and in the second part (part B) the model-based and transform-based methods will be presented. Statistical methods work based on statistical quantities that obtained from image pixels intensities, while structural methods operate based on texture primitives (a group of pixels with almost the same intensities). In the present paper, the applications of statistical image texture evaluation methods in food industries were investigated. Therefore, at first mechanisms of different statistical texture evaluation methods have been presented. Then, examples of recent studies related to employments of statistical image texture in quality control of food products have been reviewed. The results of the previous studies indicate that statistical methods are the most popular texture evaluation methods in food industries. This popularity is due to their highly accurate performances.
کلیدواژهها [English]
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