Rice Quality Grading System Using Automated Image Analysis

영상분석법에 의한 쌀 품위 자동분석

Goodman, D. E. and R. M. Rao. 1984a. New, Rapid, Interactive Image Analysis Method for Determining Physical Dimensions of Milled Rice Kernels. J. Food Sci. 49 : 648-649 https://doi.org/10.1111/j.1365-2621.1984.tb12491.x
Masaki, O. and H. Kokichi. 1988. An application of computer image analysis for characterization of plant type in rice cultivates. Japan J. Breed. 38 : 449-458
Sakai, N., S. Yonekawa, and A. Matsuzaki. 1996. Two-dimensional image analysis of shape of rice and its application to separating varieties. J. Food Eng. 27 : 397-407 https://doi.org/10.1016/0260-8774(95)00022-4
Chtioui, Y., D. Bertrand, and D. Barba. 1996. Reduction of the size of the learning data in a probabilistic neural network by hierarchical clustering. Application to the discrimination of seeds by artificial vision. Chemom. Intell, Lab. Syst. 35 : 175-186 https://doi.org/10.1016/S0169-7439(96)00065-2
Hunter, A. 2000. Feature selection using probabilistic neural networks. Neural Comput. & Applic., 9 : 124-132 https://doi.org/10.1007/s005210070023
Visen, N. S., J. Paliwal, D. S. Jayas, and N. D. G. White. 2002. Specialist neural networks for cereal grain classification. Biosystems Engin. 82(2) : 151-159 https://doi.org/10.1006/bioe.2002.0064
Kawamura, S., M. Natsuga, K. Takekura, and K. Itoh. 2003. Development of an automatic ricequality inspection system. Comp. and Elect. in Agric. 40 : 115-126 https://doi.org/10.1016/S0168-1699(03)00015-2