Watershed-based multiscale segmentation method for color images using automated scale selection

2005 ◽  
Vol 14 (3) ◽  
pp. 033007 ◽  
Author(s):  
Iris Vanhamel
2011 ◽  
Vol 78 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Xin Zhang ◽  
Daoliang Li ◽  
Wenzhu Yang ◽  
Jinxing Wang ◽  
Shuangxi Liu

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Yunyun Yang ◽  
Boying Wu

This paper presents a new and fast multiphase image segmentation model for color images. We propose our model by incorporating the globally convex image segmentation method and the split Bregman method into the piecewise constant multiphase Vese-Chan model for color images. We have applied our model to many synthetic and real color images. Numerical results show that our model can segment color images with multiple regions and represent boundaries with complex topologies, including triple junctions. Comparison with the Vese-Chan model demonstrates the efficiency of our model. Besides, our model does not require a priori denoising step and is robust with respect to noise.


Author(s):  
Yulong Cai ◽  
Siheng Mi ◽  
Jiahao Yan ◽  
Hong Peng ◽  
Xiaohui Luo ◽  
...  

2015 ◽  
Vol 743 ◽  
pp. 293-302 ◽  
Author(s):  
G.Q. Ma ◽  
Y.C. Tian ◽  
X.L. Li ◽  
K.Z. Xing ◽  
Su Xu

The color live fish image segmentation is a important procedure of the understanding fish behavior. We have introduced an simple segmentation method of live Grouper Fish color images with seawater background and presented a segmentation framework to extract the whole fish image from the complex background of seawater. Firstly, we took true color pictures of live Grouper fish in seawater using waterproof camera and save these pictures files as RGB format files, called True-color Images. Secondly, we extracted R,G and B planes of a true color Grouper fish image, painted and compared their histograms of R,G and B planes. Thirdly, we segmented these RGB images and the R,G and B planes of a true color Grouper fish image with the k-means clustering algorithm, using the kmeans () function which is packaged by the Clustering Analysis ToolBox of Matlab 2012(a). Finally, we analyzed the relationships between these histograms and segmented images, and then got a conclusion is that : using the B plane of these RGB images as Input-matrix to do clustering segmentation algorithm by the kmeans () function of Matlab Clustering ToolBox, can got a fulfilling segmentation results.


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