Real-time object-based image registration using improved MRAN

The registration of images from cameras of different types and/or at different locations is of great interest for both military and civilian applications. Most available techniques are pixel level registration and use intensity correlation to spatially align pixels from the two cameras. Lots of computation is consumed to operate on each pixel of the images and as a result, it would be difficult to register the images in real time. Furthermore, images from different types of cameras may have different intensity distributions for corresponding pixels which will degrade the registration accuracy. In this paper we propose to use improved Minimal Resource Allocation Network (MRAN) to solve the image registration problem from two cameras. Potential features are added to improve the performance of MRAN. There are two main contributions in this paper - First, weights going directly from inputs to outputs are introduced and these parameters are updated by including in the extended Kalman filter algorithm. Second, initial number of hidden units for the sequential training of MRAN are specified and the means of the initial hidden units are precalculated using Self Organizing Maps. The experimental results show that the proposed algorithm peforms very well both in speed and accuracy.