.png){kind=icon}
返回
An Efficient MRF Embedded Level Set Method for Image Segmentation
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2014.2372615.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a fast and robust level set method for image segmentation. To enhance the robustness against noise, we embed a Markov random field (MRF) energy function to the conventional level set energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them to fall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraic multigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain, respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of our method for big image databases. By comparing the proposed fast and robust level set method with the standard level set method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medical images, and natural images, we comprehensively demonstrate the new method is robust against various kinds of noises. In particular, the new level set method can segment an image of size 500 x 500 within 3 s on MATLAB R2010b installed in a computer with 3.30-GHz CPU and 4-GB memory.
Keyword:
Level set
Markov random field
algebraic multigrid
sparse field method
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
Development and Validation of the University of Washington Clinical Assessment of Music Perception Test华盛顿大学音乐知觉临床评估测试的开发和验证
OAAI
.png){kind=icon}
OAAI分享收藏FUNCTIONAL ORGANIZATION OF TEMPORAL LOBE OF MONKEY (MACACA MULATTA) AND CHIMPANZEE (PAN SATYRUS)
PREAI
分享收藏
查看更多内容