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RANSAC算法的自适应T_(c,d)预检验 被引量：20

Enhanced RANSAC with Adaptive Pre-verification
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摘要 随机抽样一致性算法是计算机视觉领域应用最广泛的鲁棒性算法。为了进一步提高RANSAC算法的运算速度,首先在介绍RANSAC算法的Tc,d预检验加速模型的基础上,提出了一种两步法用来实现优化的预检验参数选择;然后基于这种优化选择方法提出了自适应Tc,d预检验的新算法,从而实现了不依赖用户选择的RANSAC算法的自适应加速。基于窄基线和宽基线图像对的极线几何计算的实验表明,该新算法相对于标准RANSAC算法的运算速度平均提升超过了400%。 RANSAC is the most widely used robust regression algorithm in computer vision. Starting from the Tc,d preevaluation model of RANSAC algorithm,a two-step method is presented for optimal （c,d） selection. Based on this method, the adaptive To.a test extension is proposed to achieve user independent RANSAC acceleration. We show experimentally that using both short-baseline and wide-baseline epipolar geometry estimation, the proposed method is up to 400% faster than the standard RANSAC.
作者 田文 王宏远 徐帆 方磊
机构地区 华中科技大学电子与信息工程系
出处 《中国图象图形学报》 CSCD 北大核心 2009年第5期973-977,共5页 Journal of Image and Graphics
关键词 随机抽样一致性算法 预检验 鲁棒性估计 基础矩阵 局部优化 random sample consensus （RANSAC）, pre-evaluation, robust estimation, fundamental matrix, local optimization
分类号 TP391.4 [自动化与计算机技术—计算机应用技术]
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参考文献16

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二级参考文献9

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共引文献104

同被引文献124

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引证文献20

二级引证文献148

中国图象图形学报
2009年 第5期

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