Rice is one of the most important stable foods in Iran. Sometimes, for reason such as illegal profit, it is probable a commercial rice variety with good quality properties be mixed with some low quality properties that have great similarity in appearance. In this paper, an expert system for rice purity detection based on extracted texture features of bulk samples and modeling by a multilayer neural network has been introduced. First, images of bulk samples are taken using a black box. Then, texture features is extracted. In the next step, the best features are selected using a genetic algorithm approach. Finally, a neural network based regression is used for modeling of proposed approach. The best performance is obtained using local binary pattern. To increase the efficiency of the proposed approach, the results of previous section is combined using a majority voting approach. The result of this study can be used for construction of rice purity detection system.
Mousavirad, S. J., & Akhlaghian Tab, F. (2013). Design of an expert system for Rice Purity detection using combination of texture features of bulk samples. Journal of Machine Vision and Image Processing, 1(1), 11-18.
MLA
Seyed Jalaleddin Mousavirad; Fardin Akhlaghian Tab. "Design of an expert system for Rice Purity detection using combination of texture features of bulk samples". Journal of Machine Vision and Image Processing, 1, 1, 2013, 11-18.
HARVARD
Mousavirad, S. J., Akhlaghian Tab, F. (2013). 'Design of an expert system for Rice Purity detection using combination of texture features of bulk samples', Journal of Machine Vision and Image Processing, 1(1), pp. 11-18.
VANCOUVER
Mousavirad, S. J., Akhlaghian Tab, F. Design of an expert system for Rice Purity detection using combination of texture features of bulk samples. Journal of Machine Vision and Image Processing, 2013; 1(1): 11-18.
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