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基于分形維數(shù)的玉米和雜草圖像識(shí)
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Identification of Corn and Weed Based on Fractal Dimension
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    提出了利用分形維數(shù)來識(shí)別玉米和雜草的方法,。將田間采集到的原始圖像轉(zhuǎn)化到HSI空間,利用H分量的不變特性進(jìn)行圖像變換,,以消除光照的影響,,有利于圖像的分割處理。為了識(shí)別出玉米和雜草,,比較了3種分形維數(shù)的計(jì)算公式和計(jì)算方法,,利用Matlab編寫的分形軟件得到了玉米和雜草的平均分形維數(shù),試驗(yàn)結(jié)果表明:Bouligand-Minkowski方法最佳,,其中玉米和雜草的平均分形維數(shù)分別為1.204和1.079,。利用SVM方法進(jìn)行識(shí)別,正確率可以達(dá)到

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    80%,。A method of identifying corn and weed using fractal dimension was developed. The obtained original images in the field had to be transformed to the HSI space at first. Image transformation was done using the non-variety of H channel in the HSI space in order to reduce effects from illumination changes,which was in favor of image segmentation. In order to identify corn and weed, three computational formulas of fractal dimension were proposed and compared. Mean fractal dimensions of corn and weed were obtained by fractal software which was programmed using Matlab software. The result shows that the Bouligand-Minkowski method was more effective than other methods, and mean fractal dimension of corn and weed was equal to 1.204 and 1.079 respectively. The identify accuracies of method based on SVM reach 80%.

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吳蘭蘭,劉儉英,文友先.基于分形維數(shù)的玉米和雜草圖像識(shí)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2009,40(3):176-179. Identification of Corn and Weed Based on Fractal Dimension[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(3):176-179.

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