Name
Abstract | ||
|---|---|---|
In this work, a new similarity measure between images is presented, which is based on the concept of predictability of random variables evaluated through kernel functions. Image registration is achieved maximizing this measure, analogously to registration methods based on entropy, like mutual information and normalized mutual information. Compared experimentally with these methods in different problems, our proposal exhibits a more robust performance specially for problems involving large transformations and in cases where the registration is done using a small number of samples, such as in nonparametric registration. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.cviu.2008.02.001 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
image registration,parametric and nonparametric transformations,mutual information,normalized mutual information,nonparametric registration,large transformation,new similarity measure,kernel function,multimodal image registration,information measures,different problem,gini entropy,random variable,registration method | Kernel (linear algebra),Computer vision,Similarity measure,Pattern recognition,Image retrieval,Image processing,Artificial intelligence,Mutual information,Kernel method,Mathematics,Image registration,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
112 | 2 | Computer Vision and Image Understanding |
Citations | PageRank | References |
1 | 0.36 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Héctor Fernando Gómez García | 1 | 3 | 1.40 |
| José L. Marroquín | 2 | 186 | 19.62 |
| Johan Van Horebeek | 3 | 20 | 3.38 |