Exploiting Uncertainty Propagation in Gradient-based Image Registration

2008 ◽  
Author(s):  
K. Koeser ◽  
R. Koch
Keyword(s):  
Image Registration ◽  
Uncertainty Propagation ◽  
Gradient Based

Robust CCD and IR Image Registration Using Gradient-Based Statistical Information

2010 ◽  
Vol 17 (4) ◽  
pp. 347-350 ◽  
Author(s):  
Jae Hak Lee ◽  
Yong Sun Kim ◽  
Duhgoon Lee ◽  
Dong-Goo Kang ◽  
Jong Beom Ra
Keyword(s):  
Image Registration ◽  
Statistical Information ◽  
Gradient Based

scholarly journals Application and Evaluation of Surrogate Models for Radiation Source Search

Algorithms ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 269 ◽  
Author(s):  
Jared A. Cook ◽  
Ralph C. Smith ◽  
Jason M. Hite ◽  
Razvan Stefanescu ◽  
John Mattingly
Keyword(s):  
Bayesian Inference ◽  
Radiation Source ◽  
Transport Theory ◽  
Uncertainty Propagation ◽  
Simulation Models ◽  
Surrogate Models ◽  
Multivariate Adaptive Regression Splines ◽  
Boltzmann Transport ◽  
Boltzmann Transport Theory ◽  
Gradient Based

Surrogate models are increasingly required for applications in which first-principles simulation models are prohibitively expensive to employ for uncertainty analysis, design, or control. They can also be used to approximate models whose discontinuous derivatives preclude the use of gradient-based optimization or data assimilation algorithms. We consider the problem of inferring the 2D location and intensity of a radiation source in an urban environment using a ray-tracing model based on Boltzmann transport theory. Whereas the code implementing this model is relatively efficient, extension to 3D Monte Carlo transport simulations precludes subsequent Bayesian inference to infer source locations, which typically requires thousands to millions of simulations. Additionally, the resulting likelihood exhibits discontinuous derivatives due to the presence of buildings. To address these issues, we discuss the construction of surrogate models for optimization, Bayesian inference, and uncertainty propagation. Specifically, we consider surrogate models based on Legendre polynomials, multivariate adaptive regression splines, radial basis functions, Gaussian processes, and neural networks. We detail strategies for computing training points and discuss the merits and deficits of each method.

Intensity Gradient Based Registration and Fusion of Multi-modal Images

2007 ◽  
Vol 46 (03) ◽  
pp. 292-299 ◽  
Author(s):  
J. Modersitzki ◽  
E. Haber
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Numerical Optimization ◽  
Distance Measure ◽  
State Of The Art ◽  
Intensity Gradient ◽  
Alternative Approach ◽  
Main Disadvantage ◽  
Gradient Based ◽  
Multimodal Images

Summary Objectives: A particular problem in image registration arises for multi-modal images taken from different imaging devices and/or modalities. Starting in 1995, mutual information has shown to be a very successful distance measure for multi-modal image registration. Therefore, mutual information is considered to be the state-of-the-art approach to multi-modal image registration. However, mutual information has also a number of well-known drawbacks. Its main disadvantage is that it is known to be highly non-convexand hastypicallymanylocal maxima. Methods: This observation motivates us to seek a different image similarity measure which is better suited for optimization but as well capable to handle multimodal images. Results: In this work, we investigate an alternative distance measure which is based on normalized gradients. Conclusions: As we show, the alternative approach is deterministic, much simpler, easier to interpret, fast and straightforward to implement, faster to compute, and also much more suitable to numerical optimization.

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