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基于簡(jiǎn)化脈沖耦合神經(jīng)網(wǎng)絡(luò)的蝗蟲圖像二值分割
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    摘要:

    采用參數(shù)簡(jiǎn)化的脈沖耦合神經(jīng)網(wǎng)絡(luò)(PCNN)分割圖像,進(jìn)行了蝗蟲圖像分割實(shí)驗(yàn),,區(qū)域正確識(shí)別率達(dá)94%,,為蝗蟲自動(dòng)偵測(cè)系統(tǒng)中的數(shù)據(jù)處理提供了技術(shù)支持,。計(jì)算機(jī)仿真表明,,采用PCNN圖像分割算法,,圖像中的目標(biāo)(蝗蟲)易于發(fā)現(xiàn),分割效果明顯好于采用開(kāi)操作處理的圖像,。

    Abstract:

    Image binary segmentation is the most fundamental and important preprocessing in image analysis and pattern recognition, which directly affects analyses and results of post-processing. The crucial step in image data processing of automatic locust detection system (ALDS) is image segmentation. A parametrically simplified pulse-coupled neural network (PCNN) was brought forward. Experiments were done on locust images. Area recognition rate (ARR) achieved 94%. The results of computer simulation showed that the objects (locusts) in the image were easier to be found by using PCNN than by the‘open’operation. The performance of PCNN in image processing has been tested, and a new approach to detect locusts has been developed.

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熊雪梅,王一鳴,張小超,鄭永軍.基于簡(jiǎn)化脈沖耦合神經(jīng)網(wǎng)絡(luò)的蝗蟲圖像二值分割[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2007,38(10):84-86.[J]. Transactions of the Chinese Society for Agricultural Machinery,2007,38(10):84-86.

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