Fast Local and Global Projection-Based Methods for Affine Motion Estimation
Journal of Mathematical Imaging and Vision
Fast adaptive nonuniformity correction for infrared focal-plane array detectors
EURASIP Journal on Applied Signal Processing
A MAP estimator for simultaneous superresolution and detector nonuniformity correction
EURASIP Journal on Applied Signal Processing
Multisource data registration based on NURBS description of contours
International Journal of Remote Sensing
The role of intensity standardization in medical image registration
Pattern Recognition Letters
Stitching algorithms for biological specimen images
International Journal of Computational Vision and Robotics
Edge projection-based image registration
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
A novel projection based approach for medical image registration
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
Multidimensional Systems and Signal Processing
Level-line primitives for image registration with figures of merit
Integrated Computer-Aided Engineering
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A computationally efficient method for image registration is investigated that can achieve an improved performance over the traditional two-dimensional (2-D) cross-correlation-based techniques in the presence of both fixed-pattern and temporal noise. The method relies on transforming each image in the sequence of frames into two vector projections formed by accumulating pixel values along the rows and columns of the image. The vector projections corresponding to successive frames are in turn used to estimate the individual horizontal and vertical components of the shift by means of a one-dimensional (1-D) cross-correlation-based estimator. While gradient-based shift estimation techniques are computationally efficient, they often exhibit degraded performance under noisy conditions in comparison to cross-correlators due to the fact that the gradient operation amplifies noise. The projection-based estimator, on the other hand, significantly reduces the computational complexity associated with the 2-D operations involved in traditional correlation-based shift estimators while improving the performance in the presence of temporal and spatial noise. To show the noise rejection capability of the projection-based shift estimator relative to the 2-D cross correlator, a figure-of-merit is developed and computed reflecting the signal-to-noise ratio (SNR) associated with each estimator. The two methods are also compared by means of computer simulation and tests using real image sequences