State Recognition of Egg Embryo in Vaccines Production Based on SVM

摘要

The method for state recognition of egg embryo in biological vaccines production based on computer image processing and support vector machine was researched. Firstly, the median filtering method was applied to eliminate noises in the images of egg embryo that were classified by the requirements of practical production, then the threshold segmentation method was used to segment the images, then the blood vessels and black blocks in the egg embryo images were selected as major characteristics to extract. The decision tree classification model with structure of binary tree was built on support vector machine, and the model was trained with the toolkit LIBSVM, the penalty factor C = 2, RBF kernel parameter Υ=3.0512e-05 and the precision of cross validation accuracy is 98.913%. Finally, 100 egg embryos were randomly selected as the test samples to do pattern recognition, and compared with manual test result. The comparison result showed that the discriminant accuracy of SVM classifier on weak embryo, polluted embryo or dead embryo was 100^{%, and it of SVM classifier on live embryo was 94.62%.}