文件名称:Novel-robust-and-self-adaptive-road-following-algo
- 所属分类：
- 图形图像处理(光照,映射..)
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- [PDF]
- 上传时间：
- 2012-11-26
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- 2.08mb
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- 0次
- 提 供 者：
- 黄*
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本文提出一种基于改进图模型的自适应道路跟踪算法,利用基于边缘置信度的均值偏移
算法,将图像划分为具有准确边界的若干同质区域,以这些区域为结点构建改进图模型,然后根据道路/非路模型统计信息,采用
Graph Cut 方法获得最终的二值图。该算法将Graph Cut 和均值偏移方法有效融合,以克服各自缺点,并通过道路/非路模型自更
新使得该算法可有效适应室外环境下复杂场景变化。-Two dimension road following is a crucial task of vision navigation for mobile robots. Because road
environments are usually complex, robust and continuous road following based on two-dimension image sequence
is still a challenging task. This paper proposes a self-adaptive road following algorithm based on an improved
graph model. Firstly, the mean shift algorithm embedded with edge confidence is used to partition the images into
homogenous regions with precise boundary, and an improved graph model is constructed with these regions.
算法,将图像划分为具有准确边界的若干同质区域,以这些区域为结点构建改进图模型,然后根据道路/非路模型统计信息,采用
Graph Cut 方法获得最终的二值图。该算法将Graph Cut 和均值偏移方法有效融合,以克服各自缺点,并通过道路/非路模型自更
新使得该算法可有效适应室外环境下复杂场景变化。-Two dimension road following is a crucial task of vision navigation for mobile robots. Because road
environments are usually complex, robust and continuous road following based on two-dimension image sequence
is still a challenging task. This paper proposes a self-adaptive road following algorithm based on an improved
graph model. Firstly, the mean shift algorithm embedded with edge confidence is used to partition the images into
homogenous regions with precise boundary, and an improved graph model is constructed with these regions.
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融合图切割和聚类算法的鲁棒自适应道路跟踪.pdf