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Image registration is a fundamental procedure in image processing that aligns two or more images of the same scene taken from different times, different viewpoints, or even different sensors. It is reasonable to orientate two images by matching corresponding pixels or regions that are considered identical. Based on this concept, this paper proposes a novel image registration method that applies the information theorem on intensity difference data. An entropy-based objective function is then developed according to the histogram of the intensity difference. The intensity difference represents the absolute gray-level difference of the corresponding pixels between the reference and sensed images over the overlapped region. The proposed registration method is to align the sensed image onto the reference image by minimizing the entropy of the intensity difference through iteratively updating the parameters of the similarity transformation. For performance evaluation, the proposed method is compared with the two exiting registration methods in terms of eight test image sets. The experiment is divided into two scenarios. One is to investigate the sensitivity (i.e., robustness) of the objective functions in these three different methods; the other is to verify the effectiveness of the proposed method. Through the experimental results, the proposed method is shown to be very effective in image registration and outperforms the other two methods over the test image sets.

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This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.