关键词: 相似度, 背景减除, 灰度形态学, LBP, Mean Shift, 异物自动检测

Abstract: It is difficult to track the impurities, which land fast and are very similar in transfusion bottles. In this paper, an impurity detection method is proposed to solve this problem by embedding linear prediction and[FLBPriu28,1], which means five uniform texture models of Local Binary Pattern related to the edge and corner, into Mean shift algorithm. The background of image sequences is established using the simplified normalization cross correlation coefficients. The exact positions of the impurities are extracted applying background subtraction, gray image morphology, and maximum contrast segmentation. The improved Mean Shift is used to track motional impurities in a few successive frames to ensure detection accuracy. The experimental results show that the average detection rate is 96.3% for the impurity larger than two pixels in diameter. The average detection time is 0.8 seconds per transfusion bottle.

Key words: similarity, background subtraction, gray image morphological, LBP, Mean Shift, impurity automatically detection