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基于參數(shù)自適應(yīng)脈沖耦合神經(jīng)網(wǎng)絡(luò)的黃瓜目標(biāo)分割
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國(guó)家高技術(shù)研究發(fā)展計(jì)劃(863計(jì)劃)資助項(xiàng)目(2006AA10Z259);中央高校基本科研業(yè)務(wù)費(fèi)自主創(chuàng)新資助項(xiàng)目(KYZ201006);南京農(nóng)業(yè)大學(xué)青年科技創(chuàng)新基金資助項(xiàng)目(KJ09030)


Cucumber Image Segmentation Based on Weighted Connection Coefficient Pulse Coupled Neural Network
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    摘要:

    對(duì)脈沖耦合神經(jīng)網(wǎng)絡(luò)的參數(shù)進(jìn)行簡(jiǎn)化，并自適應(yīng)確定各參數(shù)，將圖像的空間信息和灰度信息耦合到加權(quán)耦合連接系數(shù)中，進(jìn)行溫室黃瓜圖像分割，采用二維Tsallis熵選擇最佳迭代結(jié)果。試驗(yàn)結(jié)果表明：用區(qū)域?qū)Ρ榷龋℅C）和區(qū)域一致性（UC）評(píng)價(jià)方法評(píng)價(jià)，該方法的分割效果好于采用香農(nóng)熵和最小交叉熵終止迭代的標(biāo)準(zhǔn)脈沖耦合神經(jīng)網(wǎng)絡(luò)分割效果。

    Abstract:

    Parameters of pulse coupled neural network(PCNN) were simplified and adaptive to determine. Spatial information and gray information of image were coupled to the weighted connection coefficient for greenhouse cucumber segmentation by using the 2-D Tsallis entropy to select the best results of iteration. Experimental results showed that, methods of contrast and regional consistency were employed to evaluate effect of different segmentation. Segmentation results of prospered method was better than using Shannon entropy and minimum cross entropy to terminate iteration of standard pulse coupled neural network segmentation.

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王海青,姬長(zhǎng)英,顧寶興,田光兆.基于參數(shù)自適應(yīng)脈沖耦合神經(jīng)網(wǎng)絡(luò)的黃瓜目標(biāo)分割[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2013,44(3):204-208. Wang Haiqing, Ji Changying, Gu Baoxing, Tian Guangzhao. Cucumber Image Segmentation Based on Weighted Connection Coefficient Pulse Coupled Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(3):204-208.

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  • 在線發(fā)布日期: 2013-02-25
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