1. Paper Review Zhiqiang 9/21/12
GPU Accelerated Edge-Region Based Level Set Evolution Constrained by 2D Gray-scale Histogram
Paper Review Zhiqiang 9/21/12

2. Background– Active contour for image segmentation
Active contour and it’s level set implementation
Pro: Topological changes are handled naturally
Image to be segmented
Level set distance function
Initial contour

3. Background– Active contour for image segmentation
CV-MODEL (region based active contour model)
Its main idea is to consider the information inside the regions, and not only at their edge. Energy function (or cost function):
where u is the distance function. And C is represented as the zero level set of u. Minimizing with gradient descent flow method

4. Research Problem -- weakness of region based model
Example which success
Example which fall

5. Research Problem -- Advantage of edge based model
GAC-MODEL (Edge based active contour model)
Original image I
g is an edge-stopping function defined as follow:
g(I)

6. Research Problem -- Advantage of edge based model
success
failure

7. Claimed Contribution
New model which use simultaneously edge, region and 2D histogram information in order to efficiently segment objects of interest in a given scene
Lattice Boltzmann Method (LBM) is proposed to compute the model in parallel

8. Edge and region estimate
Edge detector: The diffusivity coefficient g(I) is adapted to the image itself. g(I) is large when 𝛻𝐼 is small on intra regions. And g(I) become small when 𝛻𝐼 is large near edges.(same with GAC model)
Region detector: Inter-class Variance. (same with CV model)

9. Speed control
Region selector: Using different evolution speed in various regions based on gradient histogram analysis.
Diffusion equation with a body force:

10. Experiment results
GPU implementation: Parallel computing toolbox of matlab R2012a and NVIDIA GPU GT 430.(ignore the dates transferring time between CPU and GPU)

11. Contribution analysis
The proposed model isn’t novel, And segmentation results seem not to be art of the state.
Considering other work that focus on using LBM for active contour model, What’s the unique contribution of this paper?

12. Algorithm analysis
LBM (step 4) may be very fast on GPU, but the computing of image features which involve statistical information would be time consuming.
Which parts of algorithm is computed on GPU?
Computing time for each step?

13. Questions?