Flooding region growing: a new parallel image segmentation model based on membrane computing

2020 ◽  
Author(s):  
Mehran Dalvand ◽  
Abdolhossein Fathi ◽  
Arezoo Kamran
Keyword(s):  
Image Segmentation ◽  
Membrane Computing ◽  
Region Growing ◽  
Model Based ◽  
Parallel Image

Towards distributed region growing image segmentation based on MapReduce

2015 ◽  
Author(s):  
P. N. Happ ◽  
R. S. Ferreira ◽  
G. A. O. P. Costa ◽  
R. Q. Feitosa ◽  
C. Bentes ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Region Growing

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 />

FU-Net: fast biomedical image segmentation model based on bottleneck convolution layers

2021 ◽  
Author(s):  
Bekhzod Olimov ◽  
Karshiev Sanjar ◽  
Sadia Din ◽  
Awaise Ahmad ◽  
Anand Paul ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Biomedical Image ◽  
Model Based

Pixel-parallel digital CMOS implementation of image segmentation by region growing

2005 ◽  
Vol 152 (6) ◽  
pp. 579 ◽  
Author(s):  
T. Morimoto ◽  
Y. Harada ◽  
T. Koide ◽  
H.J. Mattausch
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Digital Cmos ◽  
Cmos Implementation

Visual Analytics for model-based medical image segmentation: Opportunities and challenges

2013 ◽  
Vol 40 (12) ◽  
pp. 4934-4943 ◽  
Author(s):  
Tatiana von Landesberger ◽  
Sebastian Bremm ◽  
Matthias Kirschner ◽  
Stefan Wesarg ◽  
Arjan Kuijper
Keyword(s):  
Image Segmentation ◽  
Visual Analytics ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Model Based

Region growing and region merging image segmentation

2002 ◽  
Author(s):  
N. Ikonomatakis ◽  
K.N. Plataniotis ◽  
M. Zervakis ◽  
A.N. Venetsanopoulos
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Region Merging
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