Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric partial differential equations and image analysis
Geometric partial differential equations and image analysis
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Non-linear Diffusion for Interactive Multi-scale Watershed Segmentation
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Exploring Non-linear Diffusion: The Diffusion Echo
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Probabilistic Estimation of Local Scale
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
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We assess the feasibility of approximating non-linear diffusion processes with simple local Gaussian filters. The purpose of doing this is twofold. Firstly, the theoretical implications are by themselves interesting. Secondly, a successful method would reduce the need for computationally expensive implementations of non-linear diffusion schemes. We evaluate using isotropic and affine Gaussian filters for the task of approximating the local diffusion for a number of non-linear diffusion schemes. The approximations are firstly explored using an information theoretical approach and secondly evaluated based on their performance on a multi-scale segmentation application. The results show that while the approximations do not perform quite as well as the original non-linear scheme, the decrease in performance is acceptable for the evaluated task. Furthermore, the affine approximations perform significantly better than the isotropic.