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Method for real-time deformable fusion of a source multi-dimensional image and a target multi-dimensional image of an object
Method for real-time deformable fusion of a source multi-dimensional image and a target multi-dimensional image of an object
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机译:用于对象的源多维图像和目标多维图像的实时可变形融合的方法
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摘要
The invention concerns a method for deformable fusion of a source multi-dimensional image (s(x)) and a target multi-dimensional image (t(x)) of an object, each image being defined on a multi-dimensional domain by a plurality of image signal samples, each sample having an associate position in the multi-dimensional domain and an intensity value, the method comprising estimating a smooth deformation field (d(x)) that optimizes a similarity criterion between the source image and the target image using a Markov Random Field framework, in near real-time performance. The similarity criterion is computed on transform coefficients obtained by applying a sub-space hierarchical transform to the image samples of the target image and to image samples obtained from the source image, an optimal tradeoff between a smoothness condition and the similarity criterion being automatically determined.
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机译:本发明涉及一种用于物体的源多维图像(s(x))和目标多维图像(t(x))的可变形融合的方法,其中每个图像在三维域上通过多个图像信号样本,每个样本在多维域中都有一个关联位置和一个强度值,该方法包括估算平滑变形场(d(x)),该场优化了源图像和目标图像之间的相似性标准使用Markov Random Field框架实现近乎实时的性能。在通过对目标图像的图像样本和从源图像获得的图像样本应用子空间分层变换而获得的变换系数上计算相似度标准,自动确定平滑度条件和相似度标准之间的最佳折衷。
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