Seminars_raw

Solving graph-cut based optimization problems by a smoothing technique with applications in multi-phase segmentation

2018-12-14 00:00

Speaker: Ke YIN

unit:

Time: 2018-10-27 14:30-15:30

Venue: The Wei Jin Road No. 6 Building 112 campus teaching

starttime: 2018-10-27 14:30-15:30

Profile:


Theme:
Solving graph-cut based optimization problems by a smoothing technique with applications in multi-phase segmentation
Time:
2018-10-27 14:30-15:30
Venue:
The Wei Jin Road No. 6 Building 112 campus teaching
Speaker:
Ke YIN

Profile

Huazhong University of Science and Technology(华中科技大学)
 

Abstract

      Multi-labeling problems, such as multi-phase segmentation on images, can be proposed as a graph-cut based optimization problem through modification of Pott’s model. Solving the original Pott’s model is known as a combinatorial optimization, which poses difficulty in reducing computational complexity.  The continuous max-flow approach proposed by Yuan, Tai, et al has been demonstrated to be efficient in semi-supervised multi-phase segmentation. It is formulated as a convex, yet non-smooth optimization problem. Several algorithms have been proposed, such as solving the dual problem by proximal gradient, primal-dual hybrid gradient, augmented Lagrangian method, and more recent Bregman-proximal augmented Lagrangian method. In this talk, we are going to review these algorithms and propose a new one, based on a smoothing technique for the dual problem, which has several advantages such as theoretical guarantee of convergence with proved speed and error estimate for early termination. Some numerical examples in multi-phase segmentation are shown to demonstrate its effectiveness.
 


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