Efficient convex region-based segmentation for noising and inhomogeneous patterns

DOI10.3934/ipi.2022074OpenAlexW4313316750MaRDI QIDQ2697391

Jan Muhammad, Ibrar Hussain

Publication date: 18 April 2023

Published in: Inverse Problems and Imaging (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/ipi.2022074

zbMATH Keywords

level set methodpartial differential equationsimage segmentationlocal intensity differenceaverage convolution

Mathematics Subject Classification ID

Convolution as an integral transform (44A35) Computing methodologies for image processing (68U10) Convexity of real functions of several variables, generalizations (26B25) Functional analysis techniques applied to functions of several complex variables (32A70)

Uses Software

• DeepLab

Cites Work

• A local region-based Chan-Vese model for image segmentation
• Geodesic active contour under geometrical conditions: theory and 3D applications
• Active contours driven by local image fitting energy
• Geodesic active contours
• A multiphase level set framework for image segmentation using the Mumford and Shah model
• Robust region-based active contour models via local statistical similarity and local similarity factor for intensity inhomogeneity and high noise image segmentation
• Active contours without edges
• Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification
• Image Segmentation for Intensity Inhomogeneity in Presence of High Noise

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