Adaptively active contours based on variable exponent \(L^{p(|\nabla I|)}\) norm for image segmentation
type: Item
instance of: scholarly article
title: Adaptively active contours based on variable exponent \(L^{p(|\nabla I|)}\) norm for image segmentation
zbMATH Open document ID: 1264.94029
DOI: 10.1155/2012/490879
author: Wenying Wen
author: Chuanjiang He
author: Meng Li
author: Yi Zhan
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: We propose an \(L^{p(|\nabla I|)}\)-based adaptively active contours model for image segmentation which is derived from the well-known Chan-Vese (C-V) model. Unlike the C-V model, the proposed model uses the \(L^{p(|\nabla I|)}\) (\(p(|\nabla I|) > 2\)) norm instead of the \(L^2\) norm to define the external energy and incorporates an extra internal energy into the overall energy. Due to the variable exponent \(p(|\nabla I|)\) which could fit the image gradient information adaptively, the proposed \(L^{p(|\nabla I|)}\)-based model has the hope of segmenting those images with low contrast and blurred boundaries. Experimental results show that the proposed model with \(p(|\nabla I|) > 2\) really can effectively and quickly segment those images with low contrast and blurred boundaries.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 65D18
zbMATH DE Number: 6173327
Wikidata QID: Q58911816
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/490879
OpenAlex ID: W2080279333
cites work: Q3108832
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description: scientific article; zbMATH DE number 6173327