Efficient global optimization for image registration

The image registration problem of finding a mapping that matches data from multiple cameras is computationally intensive. Current solutions to this problem tolerate Gaussian noise, but are unable to perform the underlying global optimization computation in real time. This paper expands these approaches to other noise models and proposes the Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) method, originally introduced by B.C. Cetin et al. (1993), as an appropriate global optimization method for image registration. TRUST avoids local minima entrapment, without resorting to exhaustive search by using subenergy-tunneling and terminal repellers. The TRUST method applied to the registration problem shows good convergence results to the global minimum. Experimental results show TRUST to be more computationally efficient than either tabu search or genetic algorithms