基于显著性区域检测和水平集的图像快速分割算法

doi:10.11999/JEIT170214

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Abstract In order to achieve fast and accurate segmentation of images with complicated background and weak boundaries, the re-initialization method is often adopted in the traditional level set function. However, this method has many problems such as large computation and inaccurate segmentation. Thus, combined with the saliency detection algorithm, a new image segmentation method of variable level set based on the combination of edge information and regional local information is proposed. Firstly, the saliency region of the image is detected by the cellular automata model to obtain initial boundary curve of the image. Then, an improved distance normalized level set evolution (Distance Regularized Level Set Evolution, DRLSE) model is used to combine the local information of the image into the variational energy equation, and the evolution of the curve is guided by the improved energy equation. Compared with the DRLSE, the experimental results show that the average time of the proposed algorithm only needs 2.76% of the former with further improvements in the accuracy of image segmentation.

Key words： Image segmentation Level set method Saliency detection Distance Regularized Level Set Evolution (DRLSE)

Received: 17 March 2017 Published: 14 August 2017

PACS:

TP391

Fund:The National Natural Science Foundation of China (61671077), The Natural Science Foundation of Fujian Province (2017J01739), The Scientific Research Fund of Fujian Education Department (JA15136), The Teaching Reform Project of Fujian Normal University (I201602015)

Corresponding Authors: YE Feng E-mail: yefeng@fjnu.edu.cn

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	YE Feng
	LI Wanru
	CHEN Jiazhen
	ZHENG Zihua

Cite this article:

YE Feng,LI Wanru,CHEN Jiazhen等. Image Fast Segmentation Algorithm Based on Saliency Region Detection and Level Set[J]. JEIT, 2017, 39(11): 2661-2668.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170214 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I11/2661

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