A local region-based Chan-Vese model for image segmentation

DOI10.1016/j.patcog.2011.11.019zbMath1236.68275OpenAlexW2009767800MaRDI QIDQ411944

Shigang Liu, Yali Peng

Publication date: 2 May 2012

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.11.019

zbMATH Keywords

level setimage segmentationactive contour model

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10) Machine vision and scene understanding (68T45)

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Cites Work

• Active contours driven by local image fitting energy
• Interactive image segmentation by maximal similarity based region merging
• Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations
• Geodesic active contours
• A multiphase level set framework for image segmentation using the Mumford and Shah model
• An efficient local Chan-Vese model for image 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
• Snakes, shapes, and gradient vector flow
• Minimization of Region-Scalable Fitting Energy for Image Segmentation

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