Automatic Recognition Method of Five-flowered Meat with Adaptive Threshold Based on Maximum Inter-class Variance
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Abstract:
Chemical or manual detection methods are often used in the detection of five-flowered meat. The both methods are costly and time-consuming, and the errors are large. In order to improve the accuracy of this research, reduce the cost and achieve automatic detection, we used maximum interval variance adaptive threshold approach to separate the background of R-based color layer and filtered the image by median filtering in this paper. Aiming at the non-strong contrast between fat and lean meat in the five-flower meat image, we used the adaptive histogram function with limited contrast to enhance the contrast between fatness and lean meat, and then use the maximum interval adaptive threshold method to separate the fat and lean meat regions. The experimental results on the actual image samples showed that the proposed method is more accurate than the traditional new method. It shows that our method based on automatic threshold filtering can distinguish the fat and lean meat areas effectively.
