Improving image segmentation by gradient vector flow and mean shift

被引：19

作者：

Liu, Tangwei

Zhou, Huiyu ^{[1
]}

Lin, Faquan

Pang, Yusheng

Wu, Ji

机构：

[1] Queen Mary Univ London, London E1 4NS, England

[2] Guangxi Med Univ, Nanning 530027, Peoples R China

来源：

PATTERN RECOGNITION LETTERS | 2008年 / 29卷 / 01期

关键词：

segmentation; gradient vector flow; mean shift; snake;

D O I：

10.1016/j.patrec.2007.08.015

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The classical gradient vector flow (GVF) method suffers from deficiency in the presence of other significant edges adjacent to the real boundary. In this paper, we propose an improved energy function to challenge this problem by consistently reducing the Euclidean distance between the inspected centroid of the real boundary and the estimated one of the snake. Experimental work shows the proposed framework outperforms the classical GVF algorithm in different circumstances. (C) 2007 Elsevier B.V. All rights reserved.

引用

页码：90 / 95

页数：6

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