Recently, many global stereo methods have achieved
good results by modeling a disparity surface as a Markov
random field (MRF) and by solving an optimization problem
with various techniques. However, most global methods
mainly focus on how to minimize conventional cost functions
efficiently, although it is more important to define cost
functions well to improve performance.
In this paper, we propose new symmetric cost functions
for global stereo methods. We first present a symmetric data
cost function for the likelihood and then propose a symmetric
discontinuity cost function for the prior in the MRF
model for stereo. In defining cost function, both the reference
image and the target image are taken into account to
improve performance without modeling half-occluded pixels
explicitly and without using color segmentation. The
performance improvement of stereo matching due to the
proposed symmetric cost functions is verified by applying
the proposed symmetric cost functions to the belief propagation
(BP) based stereo method. Experimental results for
standard testbed images show that the performance of the
BP based stereo method is greatly improved by the proposed
symmetric cost functions.