Practical image registration concerns overcome by the weighted and filtered mutual information metric

Keane, Tommy P.; Saber, Eli; Rhody, Harvey; Savakis, Andreas; Raj, J. Relin Francis

doi:10.1117/1.jei.21.2.023029

Cited by 1 publication

(3 citation statements)

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“…where s m G = [σx 4 , σy 4 , σxy 3 , σx 3 y, σx 2 y 2 , σx 3 , σy 3 , σxy 2 , σx 2 y, σx 2 , σy 2 , σxy] T The derivative of Eq. (11) can be computed using ( )…”

Section: Optimizationmentioning

confidence: 99%

“…By using the derivative (12), optimize the objective function (9) by quasi-Newton method, and then obtain the optimal transformation coefficient vector Q°; 4 Compute the transformation result f G (U) by using Eq. (8); --Fine registration: 5…”

Section: Optimizationmentioning

confidence: 99%

“…Image registration is a fundamental problem in many applications, such as pattern recognition, image mosaic and stereo vision, especially multi-sensor image fusion [1][2][3][4]. These applications must be on the basis of successful image registration.…”

Section: Introductionmentioning

confidence: 99%

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Non-Rigid Registration via Global to Local Transformation

Pan

Zhou

et al. 2020

Teh. vjesn.

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Non-rigid point set and image registration are key problems in plenty of computer vision and pattern recognition tasks. Typically, the non-rigid registration can be formulated as an optimization problem. However, registration accuracy is limited by local optimum. To solve this problem, we propose a method with global to local transformation for non-rigid point sets registration and it also can be used to infrared (IR) and visible (VIS) image registration. Firstly, an objective function based on Gaussian fields is designed to make a problem of non-rigid registration transform into an optimization problem. A global transformation model, which can describe the regular pattern of non-linear deformation between point sets, is then proposed to achieve coarse registration in global scale. Finally, with the results of coarse registration as initial value, a local transformation model is employed to implement fine registration by using local feature. Meanwhile, the optimal global and local transformation models estimated from edge points of IR and VIS image pairs are used to achieve non-rigid image registration. The qualitative and quantitative comparisons demonstrate that the proposed method has good performance under various types of distortions. Moreover, our method can also produce accurate results of IR and VIS image registration.

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“…where s m G = [σx 4 , σy 4 , σxy 3 , σx 3 y, σx 2 y 2 , σx 3 , σy 3 , σxy 2 , σx 2 y, σx 2 , σy 2 , σxy] T The derivative of Eq. (11) can be computed using ( )…”

Section: Optimizationmentioning

confidence: 99%

Section: Optimizationmentioning

confidence: 99%