Scene matching NCC value improvement based on contrast matching

Geometrical and radiometrical corrections are important for scene matching applications. We suppose the applications that there are no geometrical errors based on using 3D-Inertial sensors for geometrical corrections. In these cases, Normalized Cross-Correlation (NCC) is commonly used method for scene matching. The problem of matching a pattern image (mask) to an image in these cases needs to correction of radiometrical errors as illumination (contrast) variations. In this paper we show that correlation between a mask and a histogram matched image instead of using that raw version, improves the correlation value. First we match histogram function of the image to histogram function of the mask in order to have two closed contrast images, and then correlate those together using NCC and root mean square error (RMSE) methods. Simulation results confirm that according to using NCC and RMSE simultaneously, not only this method is a fast and real time method, but also according to matching histogram function of the received image to histogram function of the mask, it improves the correlation value. Also we show that using the edge detected version of the mask and histogram matched image, lead us to have the best results.