Efficient Registration of Nonrigid 3-D Bodies

We present a novel method to perform an accurate registration of 3-D nonrigid bodies by using phase-shift properties of the dual-tree complex wavelet transform (DT-\BBCWT). Since the phases of DT-\BBCWT coefficients change approximately linearly with the amount of feature displacement in the spatial...

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Bibliographic Details
Published in:IEEE transactions on image processing 2012-01, Vol.21 (1), p.262-272
Main Authors: Huizhong Chen, Kingsbury, N.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Detection, estimation, filtering, equalization, prediction
Dual-tree complex wavelet transform (\hbox{DT-}\BBC\hbox{WT})
Equations
Exact sciences and technology
Feature extraction
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Image registration
Imaging, Three-Dimensional - methods
Information, signal and communications theory
Mathematical model
Motion estimation
optical flow
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Signal and communications theory
Signal processing
Signal, noise
Subtraction Technique
Telecommunications and information theory
Wavelet transforms
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Summary:We present a novel method to perform an accurate registration of 3-D nonrigid bodies by using phase-shift properties of the dual-tree complex wavelet transform (DT-\BBCWT). Since the phases of DT-\BBCWT coefficients change approximately linearly with the amount of feature displacement in the spatial domain, motion can be estimated using the phase information from these coefficients. The motion estimation is performed iteratively: first by using coarser level complex coefficients to determine large motion components and then by employing finer level coefficients to refine the motion field. We use a parametric affine model to describe the motion, where the affine parameters are found locally by substituting into an optical flow model and by solving the resulting overdetermined set of equations. From the estimated affine parameters, the motion field between the sensed and the reference data sets can be generated, and the sensed data set then can be shifted and interpolated spatially to align with the reference datafeature displacement set.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2011.2160958