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

2005 ◽  
Vol 14 (3) ◽  
pp. 033007 ◽  
Author(s):  
Iris Vanhamel
Keyword(s):  
Color Images ◽  
Segmentation Method ◽  
Scale Selection ◽  
Multiscale Segmentation

A fast segmentation method for high-resolution color images of foreign fibers in cotton

2011 ◽  
Vol 78 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Xin Zhang ◽  
Daoliang Li ◽  
Wenzhu Yang ◽  
Jinxing Wang ◽  
Shuangxi Liu
Keyword(s):  
High Resolution ◽  
Color Images ◽  
Segmentation Method

scholarly journals A New and Fast Multiphase Image Segmentation Model for Color Images

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Yunyun Yang ◽  
Boying Wu
Keyword(s):  
Image Segmentation ◽  
A Priori ◽  
Color Images ◽  
Triple Junctions ◽  
Segmentation Method ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Piecewise Constant ◽  
Multiple Regions ◽  
Convex Image Segmentation

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.

An unsupervised segmentation method based on dynamic threshold neural P systems for color images

2021 ◽  
Author(s):  
Yulong Cai ◽  
Siheng Mi ◽  
Jiahao Yan ◽  
Hong Peng ◽  
Xiaohui Luo ◽  
...  
Keyword(s):  
Color Images ◽  
P Systems ◽  
Dynamic Threshold ◽  
Segmentation Method ◽  
Unsupervised Segmentation

Color Image Segmentation of Live Grouper Fish with Complex Background in Seawater

2015 ◽  
Vol 743 ◽  
pp. 293-302 ◽  
Author(s):  
G.Q. Ma ◽  
Y.C. Tian ◽  
X.L. Li ◽  
K.Z. Xing ◽  
Su Xu
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Color Image ◽  
Color Images ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Complex Background ◽  
Segmented Images ◽  
Rgb Images ◽  
True Color

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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