Enhanced “GrabCut” tool with blob analysis in segmentation of blooming flower images

W. Tan, Tejamaya Sunday, Yi-Fei Tan
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引用次数: 10

Abstract

This paper discusses the enhancement using blob analysis applied to automatic segmentation of “GrabCut” tool [1] for segmenting blooming flowers in color images. The automatic segmentation of “GrabCut” is used to initialize the segmentation, but the results are not effective and there is insufficient separation of foreground and background color distributions. In our proposed work, the segmented “GrabCut” image in RGB format is first converted to a binary image based on the V plane of the HSV color space. The morphology operators combining with set operations are then applied to fill up the holes of blob. This is then followed by blob filtering to eliminate the unwanted connected region. Finally, the segmented binary image is converted back to its RGB form. The proposed enhanced method achieves a more efficient extraction of blooming flower in a complex environment which cannot be trivially eliminated by the automatic segmentation of “GrabCut”.
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增强的“GrabCut”工具与blob分析在盛开的花朵图像分割
本文讨论了将blob分析增强应用于“GrabCut”工具[1]的自动分割中,用于分割彩色图像中盛开的花朵。采用“GrabCut”自动分割进行初始化分割,但分割效果不佳,前景和背景颜色分布分离不够。在我们提出的工作中,首先基于HSV色彩空间的V平面将RGB格式的“GrabCut”分割图像转换为二值图像。然后将形态学算子与集合运算相结合,对斑点的孔洞进行填充。然后进行blob滤波以消除不需要的连接区域。最后,将分割后的二值图像转换回其RGB形式。本文提出的增强方法能够在复杂的环境中更有效地提取盛开的花朵,而这种提取是“GrabCut”自动分割无法轻易消除的。
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