Local structure tensorImage interpolationSuper-resolutionEdge-directed interpolationFeature preserving image interpolation is an active area in image processing field. In this paper a new direct edge directed image super-resolution algorithm based on structure tensors is proposed. Using an isotropic Gaussian...
Kovacˇevic´, "SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization," in Proc. IEEE GRSS Workshop Hyperspectral Image Signal Process., Evol. Remote Sens. (WHISPERS), Jun. 2015, pp. 1-4....
摘要: Tutorial on structure tensors. Normally associated with image processing and structure inference, this code provides a completely encapsulated package to explain and demonstrate the use of structure tensors.收藏 引用 批量引用 报错 分享
Image analysis MRI diffusion tensor estimation Each DW image was up-sampled by a factor of 2 using trilinear interpolation to reach a voxel size of 0.3 × 0.3 × 0.3 mm3. Diffusion tensor calculations were performed prior to any registration to avoid difficult transformations of the ...
I am doing the 3d image registration and want to calculate the gradient based on structure tensor,how could I get the structure tensor of the 3d image?And how to calculate the eigenvalues and eigenvectors? Thanks! 0 Comments Sign in to comment. ...
Diffusion tensor imaging (DTI)7 is the most common such method, in which a tensor is fitted to the diffusion signal at every brain voxel. For voxels containing a single fiber population, this provides a suitable estimation of its primary orientation, but the method is incapable of properly ...
1.1. A novel dense descriptor based on structure tensor voting Structure tensor represents the magnitude and orientation information of the edges of an image.20 Though the magnitude of the edges can be changed because of contrast variation, the orientation remains the same if the illumination or co...
Tensor Processing Unit (TPU) v3 with a batch size of 1 per TPU core, hence the model uses 128 TPU v3 cores. The model is trained until convergence (around 10 million samples) and further fine-tuned using longer crops of 384 residues, larger MSA stack and reduced learning rate (see...
A novel segmentation approach using intensity and structure tensor is used for change compensation, which can effectively suppress noise while preserving the structure of an image. The cascaded framework solves the problem of noise residual caused by single-frame averaging. The classical Wiener filter ...
tensors, the learning of which has been a challenge. A recent initiative known as Quantum Natural Language Processing (QNLP) introduces a translation between the DisCoCat tensors and Variational Quantum Circuits (VQC). This offers the potential of learning these higher-order tensors more ...