Bias minimizing filter design for gradient-based image registration

2005 ◽  
Vol 20 (6) ◽  
pp. 554-568 ◽  
Author(s):  
Dirk Robinson ◽  
Peyman Milanfar
Keyword(s):  
Image Registration ◽  
Filter Design ◽  
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

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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