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黃瓜蚜蟲的圖像識別與計(jì)數(shù)方法
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Image Recognition and Counting for Glasshouse Aphis gossypii
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    摘要:

    通過分析蚜蟲區(qū)域、綠色背景和蚜葉區(qū)的G分量特點(diǎn)，建立G分量閾值確定原則，并采用G分量閾值將蚜蟲區(qū)域和非蚜蟲區(qū)域分離開。針對蚜蟲的粘連重疊問題，利用擴(kuò)展極小值閾值變換的方法對輸入圖像進(jìn)行標(biāo)記，對標(biāo)記后的圖像進(jìn)行距離變換和分水嶺分割，以去除粘連。試驗(yàn)結(jié)果表明：算法能有效地分割粘連重疊的蚜蟲，過分割率與欠分割率之和為3.14%。計(jì)數(shù)準(zhǔn)確率達(dá)到96.2%，高于直接計(jì)數(shù)的

    Abstract:

    80.7%。Characteristics of the Aphis gossypii area, background and mixed area (include Aphis gossypii area and background) were analyzed and principle of determining was establish based on the threshold G component. Then Aphis gossypii area and non-Aphis gossypii area were separated using the threshold G component. For the overlapping Aphis gossypii, the input image were marked using the minimum extension transform, then distance transform and watershed algorithm was applied to the marked image, and the overlapping was removed. Experimental results showed that this algorithm could effectively segment the overlapping Aphis gossypii. The sum of over-segmentation rate and under-segmentation rate was 3.14%. The accurate rate was 96.2%, which was higher than the direct counting.

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邱白晶,王天波,李娟娟,李坤.黃瓜蚜蟲的圖像識別與計(jì)數(shù)方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2010,41(8):151-155.Image Recognition and Counting for Glasshouse Aphis gossypii[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(8):151-155.

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