A multiphase dynamic labeling model for variational recognition-driven image segmentation

DOI10.1007/s11263-005-3676-zzbMath1286.94013OpenAlexW2081425135MaRDI QIDQ2577856

Christoph Schnörr, Daniel Cremers, Nir Sochen

Publication date: 9 January 2006

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-005-3676-z

zbMATH Keywords

variational methodsimage segmentationlevel set methodsdynamic labelingshape priorsrecognition modeling

Mathematics Subject Classification ID

Computing methodologies for image processing (68U10) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items

A discrete level set approach to image segmentation ⋮ Image segmentation with dynamic artifacts detection and bias correction ⋮ A survey and comparison of discrete and continuous multi-label optimization approaches for the Potts model ⋮ Image Segmentation with Shape Priors: Explicit Versus Implicit Representations ⋮ Convex Cardinal Shape Composition ⋮ Adaptive shape prior for recognition and variational segmentation of degraded historical characters ⋮ A higher-order active contour model of a `gas of circles' and its application to tree crown extraction ⋮ SPARSE TEMPLATE-BASED VARIATIONAL IMAGE SEGMENTATION

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nonlinear total variation based noise removal algorithms
• Approximations of shape metrics and application to shape warping and empirical shape statis\-tics
• Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
• A geometric model for active contours in image processing
• A level set approach for computing solutions to incompressible two-phase flow
• Geodesic active contours
• Shape statistics in kernel space for variational image segmentation.
• Geodesic active regions and level set methods for supervised texture segmentation
• A multiphase level set framework for image segmentation using the Mumford and Shah model
• Diffusion snakes: Introducing statistical shape knowledge into the Mumford-Shah functional
• Using prior shapes in geometric active contours in a variational framework
• Motion competition: a variational approach to piecewise parametric motion segmentation
• Optimal approximations by piecewise smooth functions and associated variational problems
• Active contours without edges
• Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification
• Computer Vision - ECCV 2004
• Computer Vision - ECCV 2004
• Computer Vision - ECCV 2004