Fast image segmentation by convex minimisation and split Bregman method

2013 ◽  
Vol 49 (17) ◽  
pp. 1073-1074
Author(s):  
W.B. Li ◽  
S.H. Song ◽  
F. Luo
Keyword(s):  
Image Segmentation ◽  
Split Bregman Method ◽  
Split Bregman

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.

scholarly journals Convex Image Segmentation Model Based on Local and Global Intensity Fitting Energy and Split Bregman Method

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Yunyun Yang ◽  
Boying Wu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Energy Functional ◽  
Split Bregman Method ◽  
Split Bregman ◽  
Proposed Model ◽  
Variational Level Set ◽  
Convex Image Segmentation ◽  
Level Set Formulation ◽  
Global Fitting

We propose a convex image segmentation model in a variational level set formulation. Both the local information and the global information are taken into consideration to get better segmentation results. We first propose a globally convex energy functional to combine the local and global intensity fitting terms. The proposed energy functional is then modified by adding an edge detector to force the active contour to the boundary more easily. We then apply the split Bregman method to minimize the proposed energy functional efficiently. By using a weight function that varies with location of the image, the proposed model can balance the weights between the local and global fitting terms dynamically. We have applied the proposed model to synthetic and real images with desirable results. Comparison with other models also demonstrates the accuracy and superiority of the proposed model.

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