Image Matching Using SIFT Features and Relaxation Labeling Technique

A probabilistic neural-network-based featurematching
algorithm for a stereo image pair is presented in
this paper, which will be useful as a constraint initializing
method for further dense matching technique. In this approach,
scale-invariant feature transform (SIFT) features are used to
detect interest points in a stereo image pair. The descriptor which
is associated with each keypoint is based on the histogram of the
gradient magnitude and direction of gradients. These descriptors
are the preliminary input for the matching algorithm. Using
disparity range computed by visual inspection, the search area
can be restricted for a given stereo image pair. Reduced search
area improves the computation speed. Initial probabilities of
matches are assigned to the keypoints which are considered as
probable matches from the selected search area by Bayesian
reasoning. The probabilities of all such matches are improved
iteratively using relaxation labeling technique. Neighboring
probable matches are exploited to improve the probability of
best match using consistency measures. Confidence measures
considering the neighborhood, unicity, and symmetry are some
validation techniques which are built into the technique presented
here for finding accurate matches. The algorithm is found to be
effective in matching SIFT features detected in a stereo image
pair with greater accuracy, and these accurate correspondences
can be used in finding the fundamental matrix which encodes
the epipolar geometry between the given stereo image pair. This
fundamental matrix can then be used as a constraint for finding
inliers that are used in matching methods for deriving dense
disparity map.