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Image Matching Using SIFT Features and Relaxation Labeling Technique-A Constraint Initializing Method for Dense Stereo Matching

DSpace at IIT Bombay

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Title Image Matching Using SIFT Features and Relaxation Labeling Technique-A Constraint Initializing Method for Dense Stereo Matching
 
Creator JOGLEKAR, J
GEDAM, SS
MOHAN, BK
 
Subject Feature
image matching
probabilistic relaxation
stereo vision
validation
HOPFIELD NETWORK
VISION
REGISTRATION
DESCRIPTORS
ALGORITHM
SCALE
 
Description A probabilistic neural-network-based feature-matching 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.
 
Publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
 
Date 2014-12-28T14:21:19Z
2014-12-28T14:21:19Z
2014
 
Type Article
 
Identifier IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 52(9)5643-5652
0196-2892
1558-0644
http://dx.doi.org/10.1109/TGRS.2013.2291685
http://dspace.library.iitb.ac.in/jspui/handle/100/16739
 
Language English