Stereo vision systems are widely used for autonomous robot navigation. Most of them apply local window based methods for real-time purposes. Normalized cross correlation （NCC） is notorious for its high computational...Stereo vision systems are widely used for autonomous robot navigation. Most of them apply local window based methods for real-time purposes. Normalized cross correlation （NCC） is notorious for its high computational cost, though it is robust to different illumination conditions between two cameras. It is rarely used in real-time stereo vision systems. This paper proposes an efficient normalized cross correlation calculation method based on the integral image technique. Its computational complexity has no relationship to the size of the matching window. Experimental results show that our algorithm can generate the same results as traditional normalized cross correlation with a much lower computational cost. Our algorithm is suitable for planet rover navigation.展开更多
提出了一种基于视差图融合的匹配方法。首先,基于归一化互相关系数(normalized cross correlation,NCC),利用多个不同尺寸的匹配窗口分别进行匹配,获取相应的视差图;然后,提出了一种左右一致性(left right consistency,LRC)和信噪比(sig...提出了一种基于视差图融合的匹配方法。首先,基于归一化互相关系数(normalized cross correlation,NCC),利用多个不同尺寸的匹配窗口分别进行匹配,获取相应的视差图;然后,提出了一种左右一致性(left right consistency,LRC)和信噪比(signal to noise ratio,SNR)相结合的置信测度,用来评价视差图中每个视差的置信水平;在此基础上,提出了一种视差图融合策略,该策略对上述多个匹配窗口获取的视差图进行加权融合,融合时既考虑了视差本身的置信水平,也兼顾了其邻域视差的影响。采用TanDEM-X的聚束立体影像进行试验,结果表明,本文方法能有效减少DEM粗差点,DEM高程精度由11.28 m提高到8.41 m。展开更多
文摘Stereo vision systems are widely used for autonomous robot navigation. Most of them apply local window based methods for real-time purposes. Normalized cross correlation （NCC） is notorious for its high computational cost, though it is robust to different illumination conditions between two cameras. It is rarely used in real-time stereo vision systems. This paper proposes an efficient normalized cross correlation calculation method based on the integral image technique. Its computational complexity has no relationship to the size of the matching window. Experimental results show that our algorithm can generate the same results as traditional normalized cross correlation with a much lower computational cost. Our algorithm is suitable for planet rover navigation.
文摘提出了一种基于视差图融合的匹配方法。首先,基于归一化互相关系数(normalized cross correlation,NCC),利用多个不同尺寸的匹配窗口分别进行匹配,获取相应的视差图;然后,提出了一种左右一致性(left right consistency,LRC)和信噪比(signal to noise ratio,SNR)相结合的置信测度,用来评价视差图中每个视差的置信水平;在此基础上,提出了一种视差图融合策略,该策略对上述多个匹配窗口获取的视差图进行加权融合,融合时既考虑了视差本身的置信水平,也兼顾了其邻域视差的影响。采用TanDEM-X的聚束立体影像进行试验,结果表明,本文方法能有效减少DEM粗差点,DEM高程精度由11.28 m提高到8.41 m。
