Search - mutual information metric

Search - mutual information metric - List

[Industry research] mutual DL : 0: The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.-The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.
Update : 2025-04-04 Size : 96kb Publisher : Harry
[Special Effects] LMI DL : 0: 实现基于四叉树分解的局部化互信息。参考文献：Image fusion performance metric based on mutual information and entropy driven quadtree decomposition-Compute the Image fusion performance metric based on mutual information and entropy driven quadtree decomposition.
Update : 2025-04-04 Size : 5kb Publisher : candy
[Special Effects] iamge-fusion-evaluate DL : 0: 进行图像融合效果评价的3个常用指标，分别为互信息，Petrovic and Xydeas Metric和quality index。-Image fusion effect evaluation of three commonly used indicators, respectively, for the mutual information Petrovic and Xydeas metrics and quality index.
Update : 2025-04-04 Size : 3kb Publisher : lijunwei