This paper presents a neural network approach to classify three-band RGB color images for automatic visual inspection of seed maize. A back-propagation neural network classifier was developed and tested. The effectiveness of the neural network classification was evaluated by comparing with two conventional statistical methods, minimum distance (MD) and maximum likelihood (ML) classifications. Experimental results showed that the BP neural network classifier, the MD classifier and ML classifier provided the overall accuracies of 93.4%, 91.5% and 96%, respectively. The neural network classifier showed a better performance than the MD and ML classifiers in classifying the shady boundaries or blurred edges of an interested class when the training samples were selected from the boundary areas of the class. This research indicated that neural networks are suitable for the pattern classification of RGB color images.
Jiancheng Jia, Jiancheng Jia,
"Pattern classification of RGB color images using a BP neural network classifier", Proc. SPIE 1989, Computer Vision for Industry, (17 December 1993); doi: 10.1117/12.164864; https://doi.org/10.1117/12.164864