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Found 7 papers, 0 papers with code
Geodesic Models with Convexity Shape Prior
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios.
An Elastica Geodesic Approach With Convexity Shape Prior
The minimal geodesic models based on the Eikonal equations are capable of finding suitable solutions in various image segmentation scenarios.
A Generalized Asymmetric Dual-front Model for Active Contours and Image Segmentation
The Voronoi diagram-based dual-front active contour models are known as a powerful and efficient way for addressing the image segmentation and domain partitioning problems.
A Region-based Randers Geodesic Approach for Image Segmentation
In this paper, we introduce a new variational image segmentation model based on the minimal geodesic path framework and the eikonal PDE, where the region-based appearance term that defines then regional homogeneity features can be taken into account for estimating the associated minimal geodesic paths.
Global Minimum for a Finsler Elastica Minimal Path Approach
In this paper, we propose a novel curvature-penalized minimal path model via an orientation-lifted Finsler metric and the Euler elastica curve.
A New Finsler Minimal Path Model With Curvature Penalization for Image Segmentation and Closed Contour Detection
This metric is non-Riemannian and asymmetric, defined on an orientation lifted space, incorporating the curvature penalty in the geodesic energy.
Anisotropic Diffusion in ITK
Anisotropic Non-Linear Diffusion is a powerful image processing technique, which allows to simultaneously remove the noise and enhance sharp features in two or three dimensional images.
