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
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.
