A New Image Segmentation Algorithm Based on Additive Operator Splitting

2013 ◽  
pp. 655-661 ◽  
Author(s):  
Jin-Ping Song ◽  
Zhan-Jiang Zhi ◽  
Yu-Xia Liu
Keyword(s):  
Image Segmentation ◽  
Operator Splitting ◽  
Segmentation Algorithm ◽  
Additive Operator ◽  
Additive Operator Splitting ◽  
Image Segmentation Algorithm

Unsupervised hierarchical image segmentation with level set and additive operator splitting

2005 ◽  
Vol 26 (10) ◽  
pp. 1461-1469 ◽  
Author(s):  
M. Jeon ◽  
M. Alexander ◽  
W. Pedrycz ◽  
N. Pizzi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Operator Splitting ◽  
Additive Operator ◽  
Additive Operator Splitting

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

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