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圖像分割在成熟茄子目標識別中的應
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Segmentation in Object Recognition of Mature Eggplant
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    摘要:

    針對在自然光生長條件下采集的茄子圖像,,采用自動取閾的算法,,分別利用圖像的灰度信息、基于R、G、B分量線性變換的3個正交彩色特征量和基于HSV彩色空間對圖像進行分割。經(jīng)Matlab仿真對比結(jié)果得出:利用Otsu算法對灰度圖進行分割,雖然對灰度直方圖進行了優(yōu)化,目標與背景的分割效果較好,,但存在局部反光的影響;采用改進的Otsu算法,,對彩色特征量(2G-R-B)/4進行分割,,可以在一定程度上消除局部反光的影響;同樣采用改進的Otsu算法對HSV彩色空間色調(diào)分量的分割,,則可以克服目標茄子表皮的反光對分割結(jié)果的影響,,取得了較好效果。以數(shù)學形態(tài)學降噪方法進一步對利用色調(diào)分割后的二值圖像進行平滑處理,,可大大改善分割效果,。

    Abstract:

    An auto threshold adaptive algorithm is suggested for image segmentation of the eggplant images acquired in natural light. The segmentation is conducted with the gray-scale, three orthogonal feature vectors based on R, G, B's linear transformation, and the hue component of HSV color model. The experiment result of the segmentation with Matlab shows that the target and background have a good separation based on Otsu algorithm and the optimization of the gray-scale histogram. However, it is also observed that there is the impact of area light reflection on the region of eggplant surface. A new segmentation experiment based on improved Otsu algorithm is conducted with the color feature vector of (2G-R-B)/4 and the result shows that the new algorithm can partially eliminate the impact of light reflection. Finally, the improved Otsu algorithm with hue are tried in the segmentation and the result shows the impact of light reflection completely is effectively removed. The segmentation effect is further improved by removing the noise of hue-segmented binary image through mathematical morphology.

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李正明,王森,孫俊.圖像分割在成熟茄子目標識別中的應[J].農(nóng)業(yè)機械學報,2009,40(Z1):105-108. Segmentation in Object Recognition of Mature Eggplant[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(Z1):105-108.

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