A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Analysis of Two-Dimensional Non-Rigid Shapes
International Journal of Computer Vision
Möbius voting for surface correspondence
ACM SIGGRAPH 2009 papers
Technical Section: Discrete Laplace-Beltrami operators for shape analysis and segmentation
Computers and Graphics
Image segmentation based on GrabCut framework integrating multiscale nonlinear structure tensor
IEEE Transactions on Image Processing
Graph nodes clustering based on the commute-time kernel
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
ACM Transactions on Graphics (TOG)
International Journal of Computer Vision
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This paper advocates a novel material-aware feature descriptor for volumetric image registration. We rigorously formulate a novel probability density function (PDF) based distance metric to devise a compact local feature descriptor supporting invariance of full 3D orientation and isometric deformation. The central idea is to employ anisotropic heat diffusion to characterize the detected local volumetric features. It is achieved by the elegant unification of diffusion tensor (DT) space construction based on local Hessian eigen-system, multi-scale feature extraction based on DT-weighted dyadic wavelet transform, and local distance definition based on PDF formulated in DT space. The diffusion, intrinsic structure-aware nature makes our volumetric feature descriptor more robust to noise. With volumetric images registration as verifiable application, various experiments on different volumetric images demonstrate the superiority of our descriptor.