基于顯著性區(qū)域檢測(cè)和水平集的圖像快速分割算法

葉鋒; 李婉茹; 陳家禎; 鄭子華

doi:10.11999/JEIT170214

基于顯著性區(qū)域檢測(cè)和水平集的圖像快速分割算法

doi: 10.11999/JEIT170214

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61671077)，福建省自然科學(xué)基金(2017J01739)，福建省教育廳項(xiàng)目(JA15136)，福建師范大學(xué)教學(xué)改革研究項(xiàng)目(I201602015)

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- 文章訪問(wèn)數(shù): 1439
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-03-17
- 修回日期: 2017-07-11
- 刊出日期: 2017-11-19

Image Fast Segmentation Algorithm Based on Saliency Region Detection and Level Set

Funds:

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)

摘要

摘要: 為了實(shí)現(xiàn)含有復(fù)雜背景和弱邊界圖像的快速準(zhǔn)確分割，傳統(tǒng)的水平集常采用重新初始化的方法，但是這種方法存在計(jì)算量大、分割不準(zhǔn)確等問(wèn)題。因此，結(jié)合顯著性區(qū)域，該文提出一種基于邊緣信息與區(qū)域局部信息結(jié)合的變水平集圖像快速分割方法。首先用元胞自動(dòng)機(jī)模型檢測(cè)出圖像的顯著性區(qū)域，得到圖像的初始化邊界曲線。然后，采用改進(jìn)的距離正規(guī)化水平集演化(Distance Regularized Level Set Evolution, DRLSE)模型把圖像的局部信息結(jié)合到變分能量方程中，用改進(jìn)的能量方程去指導(dǎo)曲線的演化。實(shí)驗(yàn)結(jié)果表明，與DRLSE模型相比，提出的算法平均消耗的時(shí)間只需要前者的2.76%，且具有較高的分割準(zhǔn)確性。
- 圖像分割 /
- 水平集方法 /
- 顯著性檢測(cè) /
- 距離正規(guī)化水平集演化
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.
- Image segmentation /
- Level set method /
- Saliency detection /
- Distance Regularized Level Set Evolution (DRLSE)

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