Abstract: 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.

Abstract: 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.

Abstract: A message passing scheme for MAP inference on Markov Random Fields based on a message computation using an intermediate input vector I, an output message vector M, an auxiliary seed vector S, all of equal length N, and a pairwise function r=d(x,y), where r, x, y are real numbers, includes: for each element j of vector S, do S(j)=j consider an index distance ?=2^floor(log 2(N)); repeat while ?>0 for each index of vector I, namely i, do in parallel: consider the set of all indices within distance ? from a given i, augmented by i; for every k belonging to this set, calculate its distance from i using the function: d(i,k)+I(S(k)); find the minimum distance and call n the index corresponding to this minimum distance do S(i)=S(n) ?=floor (?/2) for each j of vector M, do M(j)=I(S(j))+d(j,S(j)).

Abstract: A message passing scheme for MAP inference on Markov Random Fields based on a message computation using an intermediate input vector I, an output message vector M, an auxiliary seed vector S, all of equal length N, and a pairwise function r=d(x,y), where r,x,y are real numbers, includes: for each element j of vector S, do S(j)=j consider an index distance ?=2?floor(log2(N)); repeat while ?>0 for each index of vector I, namely i, do in parallel: consider the set of all indices within distance A from a given i, augmented by i; for every k belonging to this set, calculate its distance from i using the function: d(i,k)+I(S(k)); find the minimum distance and call n the index corresponding to this minimum distance do S(i)=S(n) ?=floor (?/2) for each j of vector M, do M(j)=I(S(j))+d(j,S(j)).