梁耀, 黎双文, 刘鑫磊, 李丰果. 复杂背景下目标树叶自动分割的Grabcut算法[J]. 华南师范大学学报(自然科学版), 2018, 50(6): 112-118. doi: 10.6054/j.jscnun.2018126
引用本文: 梁耀, 黎双文, 刘鑫磊, 李丰果. 复杂背景下目标树叶自动分割的Grabcut算法[J]. 华南师范大学学报(自然科学版), 2018, 50(6): 112-118. doi: 10.6054/j.jscnun.2018126
LIANG Y, NI S W, LIU X L, LI F G. The GrabCut Algorithm for the Automatic Segmentation of Target Leaves under the Complex Background[J]. Journal of South China Normal University (Natural Science Edition), 2018, 50(6): 112-118. doi: 10.6054/j.jscnun.2018126
Citation: LIANG Y, NI S W, LIU X L, LI F G. The GrabCut Algorithm for the Automatic Segmentation of Target Leaves under the Complex Background[J]. Journal of South China Normal University (Natural Science Edition), 2018, 50(6): 112-118. doi: 10.6054/j.jscnun.2018126

复杂背景下目标树叶自动分割的Grabcut算法

The GrabCut Algorithm for the Automatic Segmentation of Target Leaves under the Complex Background

  • 摘要: 针对传统GrabCut算法需要人机交互且难以在复杂背景或光照不均匀时准确分割目标树叶的缺点,提出一种基于GrabCut算法的复杂背景下或光照不均匀时目标树叶的自动分割算法。本算法利用模糊高斯混合模型(FGMM)和图像的颜色信息对原始图像进行标记实现自动分割。首先选取合适的模糊因子利用模糊高斯混合模型对图像像素进行一次标记;在一次标记的基础上再结合超绿算法(EXG)选取合适的阈值对图像像素进行二次标记;最后将二次标记图像初始化GrabCut算法实现目标树叶的自动分割。利用几种不同的样本对提出算法的有效性和错分率进行探讨。结果表明,所提出的算法可以实现复杂背景下或光照不均匀时目标树叶的自动分割,且平均错分率达到1.625。

     

    Abstract: Since the traditional GrabCut algorithm needs to manually select the target area and it is also difficult to accurately segment the target leaves from their image with the complex background or the uneven illumination, an automatic segmentation algorithm based on the GrabCut algorithm is proposed. Combining with the fuzzy Gaussian mixture model (FGMM) and the leaf color information, this algorithm can automatically segment the target leaves from their image with the complex background or the uneven illumination. Firstly, using the appropriate fuzzy factor, the image pixels are labeled by the fuzzy Gaussian mixture model and it is used as a first-labeled image. The second-labeled image which is obtained by the Extra-green algorithm (EXG) with the appropriate threshold is used to initialize the GrabCut algorithm and the segmentation of the target leaves is accomplished automatically. The validity and the misclassification rate of the algorithm are investigtaed using several different samples. The results show that this algorithm can automatically segment the target leaves under the complex natural background or the uneven illumination and the average error rate is 1.625.

     

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