Diffusion snakes: Introducing statistical shape knowledge into the Mumford-Shah functional
From MaRDI portal
Publication:1859699
DOI10.1023/A:1020826424915zbMath1012.68785OpenAlexW1506879382MaRDI QIDQ1859699
Joachim Weickert, Daniel Cremers, Christoph Schnörr, Florian Tischhäuser
Publication date: 19 February 2003
Published in: International Journal of Computer Vision (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1023/a:1020826424915
variational methodsimage segmentationshape recognitionstatistical learninggeodesic active contoursdiffusion snake
Computing methodologies for image processing (68U10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Computing methodologies and applications (68U99) Machine vision and scene understanding (68T45)
Related Items
Intrinsic Bayesian active contours for extraction of object boundaries in images ⋮ A shape representation with elastic quadratic polynomials-preservation of high curvature points under noisy conditions ⋮ On local region models and a statistical interpretation of the piecewise smooth Mumford-Shah functional ⋮ Texture-oriented anisotropic filtering and geodesic active contours in breast tumor ultrasound segmentation ⋮ Harmonic embeddings for linear shape analysis ⋮ Image segmentation using Euler's elastica as the regularization ⋮ Figure-Ground Separation by Cue Integration ⋮ Shape statistics in kernel space for variational image segmentation. ⋮ Augmented Lagrangian method for total variation based image restoration and segmentation over triangulated surfaces ⋮ Monte Carlo methods for optimizing the piecewise constant Mumford–Shah segmentation model ⋮ Image Segmentation with Partial Convexity Shape Prior Using Discrete Conformality Structures ⋮ Regularized reconstruction of shapes with statistical a priori knowledge ⋮ Augmented Lagrangian method for an Euler's elastica based segmentation model that promotes convex contours ⋮ Multiplierless Mumford and Shah functional implementation ⋮ Image Segmentation with Shape Priors: Explicit Versus Implicit Representations ⋮ Unnamed Item ⋮ A variational model for object segmentation using boundary information and shape prior driven by the Mumford-Shah functional ⋮ A higher-order active contour model of a `gas of circles' and its application to tree crown extraction ⋮ SPARSE TEMPLATE-BASED VARIATIONAL IMAGE SEGMENTATION ⋮ Motion competition: a variational approach to piecewise parametric motion segmentation ⋮ A multiphase dynamic labeling model for variational recognition-driven image segmentation ⋮ A Characteristic Function-Based Algorithm for Geodesic Active Contours