Estimate the shift between images
#include "dip_findshift.h"
dip_Error dip_FindShift ( in1, in2, out, method, parameter )
binary, integer, float, complex
This function estimates the (sub-pixel) global shift between in1 and in2. The numbers found represent the shift of in1 with respect to in2, or the position of the first pixel of in2 in the coordinate system of in1. There are two methods that can be used: CPF and MTS. Both methods require that the shift be small. Therefore, first the integer pixel is calculated, and both images are cropped to the common part.
If method is 0, DIP_FSM_MTS is used. method can also be DIP_FSM_INTEGER_ONLY. Integer shifts can be calculated for images of any dimensionality.
The CPF method (marked as FFTS in the literature below) uses the phase of the cross-correlation (as calculated by CrossCorrelationFT) to estimate the shift. parameter sets the amount of frequencies used in this estimation. The maximum value that makes sense is sqrt(1/2). Any larger value will give the same result. Choose smaller values to ignore the higher frequencies, which have a smaller SNR and are more affected by aliasing. If parameter is set to 0, the optimal found for images sub-sampled by a factor four will be used (parameter = 0.2).
This method only supports 2-D images (until further notice).
The MTS method (marked as GRS in the literature below) uses a first order Taylor approximation of the equation in1(t) = in2(t-s) at scale parameter. Setting parameter to zero, a scale of 1 will be used. This means that the images will be smoothed with a Gaussian kernel of 1. This is the more accurate one of the two methods, and therefore is the default.
This method supports images with a dimensionality between 1 and 3.
The ITER method is an iterative version of the MTS method. It is known that a single gradient based shift estimation have bias due to truncation of the Taylor expansion series (see Pham et.al.) The bias can be expressed as a polynomial of the subpixel displacements. As a result, if Taylor method is applied iteratively and the shift is refined after each iteration, the bias eventually become negligible. By using just 3 iterations, it is possible to correct bias that results in high precision O(1e-6).
The PROJ method compute shift in each dimension from images' projections. It is fast and fairly accurate for high SNR. Should not be used for low SNR
| Data type | Name | Description |
| dip_Image | in1 | Input image |
| dip_Image | in2 | Input image |
| dip_FloatArray | out | Estimated shift |
| dipf_FindShiftMethod | method | Estimation method |
| dip_float | parameter | Parameter |
The dipf_FindShiftMethod enumeration consists of the following flags:
| Name | Description |
| DIP_FSM_DEFAULT | Default method (MTS) |
| DIP_FSM_INTEGER_ONLY | Find only integer shift |
| DIP_FSM_CPF | Use cross-correlation method |
| DIP_FSM_FFTS | Same |
| DIP_FSM_MTS | Use Taylor series method |
| DIP_FSM_GRS | Same |
C.L. Luengo Hendriks, Improved Resolution in Infrared Imaging Using Randomly Shifted Images, M.Sc. Thesis, Delft University of Technology, 1998 T.Q. Pham, M. Bezuijen, L.J. van Vliet, K. Schutte, C.L. Luengo Hendriks, Performance of Optimal Registration Estimators, In Proc. of SPIE 5817 - Visual Information Processing XIV, Defense and Security Symposium, Orlando, 2005
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