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復(fù)雜環(huán)境中蛋雞識別及粘連分離方法研究
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國家自然科學(xué)基金資助項目（31072066）和公益性行業(yè)（農(nóng)業(yè)）科研專項經(jīng)費資助項目（201003011）


Recognition and Conglutination Separation of Individual Hens Based on Machine Vision in Complex Environment
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    摘要:

    提出基于Lab顏色模型的蛋雞與背景自動分割方法和基于極限腐蝕和凹點搜尋的粘連蛋雞分離與計數(shù)算法。實驗前期將通過計算機視覺系統(tǒng)獲取的RGB圖像轉(zhuǎn)換成Lab圖像，每張圖像中均選取蛋雞及最接近蛋雞顏色的背景2個小樣本區(qū)域，分別計算這兩類區(qū)域在a、b分量的數(shù)學(xué)期望作為分割閾值。隨后將采集的圖像像素聚類于與a、b分割閾值的歐氏距離最小的區(qū)域，從而實現(xiàn)蛋雞與背景區(qū)域的自動分割。針對經(jīng)常出現(xiàn)的蛋雞群聚造成蛋雞個體之間相互粘連的情況，研究利用改進(jìn)的極限腐蝕及凹點搜尋處理算法分離出獨立的蛋雞并正確計數(shù)。108幅蛋雞圖像識別結(jié)果表明，該算法能將蛋雞個體從復(fù)雜背景中有效提取、計數(shù)和粘連分離，蛋雞計數(shù)正確率為93.5%，綜合分離正確率為89.8%。

    Abstract:

    The Lab color model was selected as the segmentation color space. The developed system was able to classify each pixel by calculating the smallest Euclidean distance between the pixel and a set of color markers. The RGB images were taken at an early stage of the experiment, and then converted to Lab images. Small regions, which include some regions from the background and the hens, were chosen. The average color of each region was calculated to segment the hen and the background. For automatically separating overlap hens and counting the number of hens in group-housed environments, an algorithm based on ultimate erosion and concavity seek was used to provide the most accurate results. With the results of 108 images, it showed that the algorithm was able to achieve an accuracy of 93.5% for counting the number of hens in image, and an accuracy of 89.8% under conglutination condition. 

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勞鳳丹,滕光輝,李卓,余禮根.復(fù)雜環(huán)境中蛋雞識別及粘連分離方法研究[J].農(nóng)業(yè)機械學(xué)報,2013,44(4):213-216,227. Lao Fengdan, Teng Guanghui, Li Zhuo, Yu Ligen. Recognition and Conglutination Separation of Individual Hens Based on Machine Vision in Complex Environment[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(4):213-216,227.

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  • 在線發(fā)布日期: 2013-03-28
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