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基于機(jī)器視覺(jué)的蘋果識(shí)別和形狀特征提
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and Shape Features Extraction of Apples Based on Machine Vision
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    摘要:

    提出了利用色差R—G和色差比(R—G)/(G—B)相結(jié)合的蘋果識(shí)別方法。在順光、逆光等不同情況下對(duì)拍攝的蘋果圖像進(jìn)行了識(shí)別,,并對(duì)識(shí)別后的圖像進(jìn)行消除噪聲、區(qū)域填充等預(yù)處理,,獲得蘋果的輪廓圖像。針對(duì)輪廓圖像,,采用遺傳算法進(jìn)行形狀特征提取,。采取多次運(yùn)行遺傳算法,并依次轉(zhuǎn)換目標(biāo)輪廓點(diǎn)為背景點(diǎn)的方法,,處理果實(shí)圖像鄰接,、重疊問(wèn)題。實(shí)驗(yàn)結(jié)果表明:蘋果識(shí)別方法在一定程度上消除了陰影,、逆光,、土壤等影響,識(shí)別率達(dá)97%?;谶z傳算法的形狀特征提取方法,,可對(duì)鄰接、重疊圖像進(jìn)行有效分割,,快速,、準(zhǔn)確地實(shí)現(xiàn)蘋果圖像圓心坐標(biāo)和半徑的提取,。

    Abstract:

    An apple recognition method with color difference R-G and color difference ratio (R-G)/(G-B) was presented.The apples in the images taken under frontlighting and backlighting conditions were recognized. The contour images were gained through the pretreatments such as noise removal, area filling etc. Shape features were extracted from the contour images based on genetic algorithm (GA). For the clustered or overlapped apples, the method of multiple running GA and converting the contour points of every apple into background in turn was applied. The experiment results showed that the proposed recognition method, to a certain extent, eliminated the influence of shade, backlighting and soil. The recognition rate reached 97%. The features extraction method based on GA segmented the clustered or overlapped apples successfully. Meanwhile, the center points and radiuses were quickly and accurately extracted.

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司永勝,喬軍,劉剛,劉兆祥,高瑞.基于機(jī)器視覺(jué)的蘋果識(shí)別和形狀特征提[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2009,40(8):161-165. and Shape Features Extraction of Apples Based on Machine Vision[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(8):161-165.

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