A survey of image registration techniques
ACM Computing Surveys (CSUR)
Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) for fast global optimization
Journal of Optimization Theory and Applications
Computer
The image processing handbook (2nd ed.)
The image processing handbook (2nd ed.)
Global Optimization for Neural Network Training
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Automatic correlation and calibration of noisy sensor readings using elite genetic algorithms
Artificial Intelligence
Multi-sensor fusion: fundamentals and applications with software
Multi-sensor fusion: fundamentals and applications with software
Robust sensor fusion algorithms: calibration and cost minimization
Robust sensor fusion algorithms: calibration and cost minimization
Feature-based image registration by means of the CHC evolutionary algorithm
Image and Vision Computing
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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 Cetin et al. 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.