基于区域过划分和再融合的全幅视觉图像分割
Segmentation of Full Vision Images Based on Region Over-Segmentation and Merging
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摘要: 研究对移动机器人采集的视觉图像进行全局精确分割问题. 针对全幅道路图像分割问题提出:首先利用区域生长算法对视觉图像在HSV颜色空间下进行基于颜色特征的全局过分割,再利用傅里叶变换提取周向谱纹理特征,根据空间位置的相邻性和周向谱的分布特性,对过分割的全幅视觉图像进行基于纹理特征的再融合. 实验结果表明,该算法能够准确实现全幅图像的分割,比单纯利用颜色或纹理特征进行全幅图像分割具有更高的精度和可靠性.Abstract: The problem of how to accurately segment the full vision images caught by robots is investigated. First over-segmentation of full vision images based on color features in HSV color space is performed. Then by taking into account the fact that texture features are not sensitive to illumination changes, the regions which have been over-segmented are merged after the texture features are extracted via Fourier transform. It is demonstrated that the proposed method can achieve full-image segmentation and is of higher precision and reliability than simply using the color or texture features.
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