gpu accelerated edge-region based level set evolution constrained by 2d gray-scale histogram

Paper Review Zhiqiang 9/21/12

GPU ACCELERATED EDGE-REGION BASED LEVEL SET EVOLUTION CONSTRAINED BY 2D GRAY-SCALE HISTOGRAM

Background– Active contour for image segmentation

Level set distance function Initial contourImage to be segmented

Active contour and it’s level set implementation Pro: Topological changes are handled naturally

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):

2

202

10)( cIcIu

udivu

t

u

)(

220)(

210

21

~

1

,,

CoutsideCinsidedxdyuHcIdxdyuHcI

dxdyuHuccE

where u is the distance function. And C is represented as the zero level set of u. Minimizing with gradient descent flow method

~

E

Research Problem -- weakness of region based model

Example which fall

Example which success

Research Problem -- Advantage of edge based model

g is an edge-stopping function defined as follow:

1

g 21 G 0I

GAC-MODEL (Edge based active contour model)

uguu

ugdiv

t

u

50 100 150 200 250 300

50

100

150

200

250

3000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Original image I

g(I)

Research Problem -- Advantage of edge based model

failure

success

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

Edge and region estimate

Edge detector: The diffusivity coefficient g(I) is adapted to the image itself. g(I) is large when is small on intra regions. And g(I) become small when is large near edges.(same with GAC model)

Region detector: Inter-class Variance. (same with CV model)

Speed control

Region selector: Using different evolution speed in various regions based on gradient histogram analysis.

Diffusion equation with a body force:

Experiment results

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

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?

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?

Questions?