  Stereo Matching is one of the classical problems in computer vision for the
extraction of 3D information but still controversial for accuracy and
processing costs. The use of matching techniques and cost functions is crucial
in the development of the disparity map. This paper presents a comparative
study of six different stereo matching algorithms including Block Matching
(BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP),
Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG), and the
proposed method. Also three cost functions namely Mean Squared Error (MSE), Sum
of Absolute Differences (SAD), Normalized Cross-Correlation (NCC) were used and
compared. The stereo images used in this study were from the Middlebury Stereo
Datasets provided with perfect and imperfect calibrations. Results show that
the selection of matching function is quite important and also depends on the
images properties. Results showed that the BP algorithm in most cases provided
better results getting accuracies over 95%.
