There are various types of existing stereo matching algorithms on image processing which applied on stereo vision images to get better results of disparity depth map. One of them is the dynamic programming method. On this research is to perform an evaluation on the performance between the dynamic programming with other existing method as comparison. The algorithm used on the dynamic programming is the global optimization which provides better process on stereo images like its accuracy and its computational efficiency compared to other existing stereo matching algorithms. The dynamic programming algorithm used on this research is the current method as its disparity estimates at a particular pixel and all the other pixels unlike the old methods which with scanline based of dynamic programming. There will be details on every existing methods presented on this paper with the comparison between the dynamic programming and the existing methods. This can propose the dynamic programming method to be used on many applications in image processing.

1.
D.
Scharstein
and
R.
Szeliski
.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
.
Tech-nical Report MSR-TR-2001-81
,
Microsoft Research
,
2001
2.
M.
Bleyer
,
M.
Gelautz
,
2005
.
A layered stereo matching algorithm using image segmentation and global visibility constraints
,
59
(
3
), pp.
128
150
.
3.
Di Stefano
,
L.
,
Mattoccia
,
S.
,
Mola
,
M.
,
2003
.
An efficient algorithm for exhaustive template matching based on normalized cross correlation
,
IEEE Computer Society
, pp.
322
327
.
4.
F.
Tombari
, et al 
2010
.
A 3D Reconstruction System Based on Improved Spacetime Stereo
, In
IEEE
, pp.
7
10
.
5.
S.
Birchfield
and
C.
Tomasi
.
Depth discontinuities by pixel-to-pixel stereo
. In
ICCV
, pp
270
293
,
1998
.
6.
O.
Sundstr
and
L.
Guzzella
,
2009
.
A Generic Dynamic Programming Matlab Function
,
1
(
7
), pp.
1625
1630
.
7.
O.
Veksler
.
Stereo Correspondence by Dynamic Programming on a Tree
. In
CVPR
, Volume
2
, pages
384
390
,
2005
.
8.
A.
Olofsson
,
2010
.
Modern Stereo Correspondence Algorithms: Investigation and evaluation
,
17 June
, pp.
5
86
.
9.
Y.
Chen
,
Y.
Hung
 et al 
2001
.
Fast Block Matching Algorithm Based on the Winner-Update Strategy
. In
IEEE
,
10
(
8
), pp.
1212
1222
10.
A.
Donate
,
Y.
Wang
 et al 
2008
.
Efficient and accurate subpixel path based stereo matching
. In
IEEE
,
8
(
6
), pp.
1
4
11.
D.
Geiger
and
F.
Girosi
.
Parallel and deterministic algorithms from MRF’s: Surface reconstruction
.
IEEE Transactions on Pattern Analysis and Machine Intelligence
,
13
(
5
):
401
412
,
May 1991
.
12.
S.
Geman
and
D.
Geman
.
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
.
IEEE Transactions on Pattern Analysis and Machine Intelligence
,
6
:
721
741
,
1984
.
13.
A.
Blake
and
A.
Zisserman
.
Visual Reconstruction
.
MIT Press
,
1987
14.
V.
Kolmogorov
and
R.
Zabih
.
Multi-camera scene recon-struction via graph cuts
. In
ECCV02
, page III:
82
ff.,
2002
.
15.
P.
Felzenszwalb
and
D.
Huttenlocher
.
Efficient belief propagation for early vision
. In
CVPR04
, pages I:
261
268
,
2004
.
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