The image simplification, noise elimination and edge enhancement steps are all fundamental to segmentation tasks. These processing techniques usually require the tuning of their control parameters; a procedure known to be incompatible with automatic segmentation. The aim of this paper is to adopt a procedure, based on nonlinear diffusion, that is capable of auto tuning by means of analytical expressions that relate diffusion times to the gradient module. The numerical method and experimental results are shown in 1D, 2D and 3D.
The image simplification, noise elimination and edge enhancement steps are all fundamental to segmentation tasks. These processing techniques usually require the tuning of their control parameters; a procedure known to be incompatible with automatic segmentation. The aim of this paper is to adopt a procedure, based on nonlinear diffusion, that is capable of auto tuning by means of analytical expressions that relate diffusion times to the gradient module. The numerical method and experimental results are shown in 1D, 2D and 3D. Read More
Related posts:
Nonlinear Diffusion Filters without Parameters for Image Segmentation
A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers
Appearance and Shape Prior Alignments in Level Set Segmentation
A Deep Learning Approach for Fear Recognition on the Edge Based on Two-Dimensional Feature Maps
A Holistic Solution for Supporting the Diagnosis of Historic Constructions from 3D Point Clouds
Exploring quorum sensing dynamics: insights from agent-based modeling on cheater evolution and noise influence