Binary image registration using cellular simultaneous recurrent networks

Cellular simultaneous recurrent networks (CSRN)s have been successfully exploited to solve the conventional maze traversing problem. In this work, for the first time, we investigate the use of CSRNs for image registration under affine transformations. In our simulations, we consider binary images with in-plane rotations between plusmn20deg. First, we experiment with a readily available CSRN with generalized multilayer perceptrons (GMLP)s as the basic core. We identify performance criteria for such CSRNs in affine correction. We then propose a modified MLP architecture with multi-layered feedback as the core for a CSRN to improve binary image registration performance. Simulation results show that while both the GMLP network and our modified network are able to achieve localized image registration, our modified architecture is more effective in moving pixels for registration. Finally, we use sub-image processing with our modified MLP architecture, to reduce training time and increase global registration accuracy. Overall, both CSRN architectures show promise for correctly registering a binary image.