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基于優(yōu)化DBSCAN算法的玉米種子純度識(shí)別
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山東省博士后創(chuàng)新基金資助項(xiàng)目（200903031）


Maize Purity Identification Based on Improved DBSCAN Algorithm
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    摘要:

    為快速有效地識(shí)別玉米種子純度，針對(duì)玉米種子圖像特征,，對(duì)玉米種子的圖像處理方法和聚類算法進(jìn)行研究,，提出一種基于最遠(yuǎn)優(yōu)先遍歷的DBSCAN玉米種子純度識(shí)別算法,。該方法首先提取玉米種子冠部核心區(qū)域的RGB、HIS和Lab種顏色模型特征參數(shù),，選取H,、S,、B作為識(shí)別向量,；其次通過最遠(yuǎn)優(yōu)先遍歷算法剔除密度差異特征向量邊緣異常散點(diǎn),；最后采用DBSCAN算法進(jìn)行密度聚類。實(shí)驗(yàn)結(jié)果表明,，該方法玉米純度識(shí)別正確率達(dá)93.3%,。

    Abstract:

    In order to identify maize purity rapidly and efficiently, the image processing technology and clustering algorithm were studied according to the maize seed and characteristics of the seed images. An improved DBSCAN on the basis of farthest first traversal algorithm (FFT) adapting to maize seeds purity identification was proposed. The color features parameters of the RGB, HIS and Lab color models of maize crown core area were extracted. H, S and B were selected to be the effective characteristic vector. The abnormal points of different density characteristic vector points were separated by FFT. Then clustering results were combined after local density cluster by DBSCAN. Experiment results showed that the method played a great role in improving the accuracy of maize purity identification. 

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劉雙喜,王盼,張春慶,王金星.基于優(yōu)化DBSCAN算法的玉米種子純度識(shí)別[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2012,43(4):188-192. Liu Shuangxi, Wang Pan, Zhang Chunqing, Wang Jinxing. Maize Purity Identification Based on Improved DBSCAN Algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery,2012,43(4):188-192.

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  • 在線發(fā)布日期: 2012-04-18
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