基于模糊层次算法的自然彩色图像粗分割

J. Maeda, S. Saga, Yukinori Suzuki
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引用次数: 4

摘要

提出了一种基于模糊层次算法的自然彩色图像粗分割方法。纹理特征采用统计几何特征(SGF),颜色特征采用L*a*b*颜色空间表示。模糊同质性决策将纹理特征和颜色特征融合在一起。提出了一种基于模糊同质性决策的分层分割方法,分层次分割、局部聚团合并、全局聚团合并和像素分类四个阶段进行。通过对自然彩色图像的分割实验,验证了该方法获得粗分割的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rough segmentation of natural color images using fuzzy-based hierarchical algorithm
This paper proposes rough segmentation of natural color images using fuzzy-based hierarchical algorithm. Statistical geometrical features (SGF) are adopted as texture features and L*a*b* color space is used to represent a color feature. Fuzzy homogeneity decision makes a fusion of texture features and color features. Proposed hierarchical segmentation method based on the fuzzy homogeneity decision is performed in four stages: hierarchical splitting, local agglomerative merging, global agglomerative merging and pixelwise classification. Experiments on segmentation of natural color images are presented to verify the effectiveness of the proposed method in obtaining rough segmentation.
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