EXAMPLES · BUGS · SEE_ALSO
imcentroid -- center sources in images, optionally find shifts
imcentroid input reference coords
- The list of images within which sources are to be centered. If a reference image is specified, imcentroid will calculate the mean X and Y shifts between the centered sources within each image and those same sources within the reference image. The input image list should normally include the reference image so that its borders are used in the calculation of the overlap region.
- reference = ""
- The reference image to which the input images will be aligned. If a reference image is specified the mean X and Y shifts between each of the input images and the reference image will be calculated, otherwise only the centers for the individual sources will be reported.
- A text file containing the coordinates of the registration objects to be centered in each image, one object per line with the x and y coordinates in columns one and two respectively. These coordinates should be measured in the frame of the reference image.
- shifts = ""
- A text file containing the initial estimate for each image of the shift in each axis relative to the reference image. These estimates are used to modify the coordinates of the registration objects prior to centering. The format of the file is one image per line with the ractional x and y shifts in columns one and two respectively. The sense of the shifts is such that: Xshift =Xref - Xin and shift= Yref - Yin. If shifts is undefined, a coarse centering pass will be made to attempt to determine the initial shifts.
- boxsize = 7
- The size in pixels of the box to use for the final centering, during which all the sources in the coords file are recentered in each image using the initial estimate of the relative shift for each image. Care should be taken to choose an appropriate value for this parameter, since it is highly data dependent.
- bigbox = 11
- The size in pixels of the box to use for coarse centering. The coarse pass through the centering algorithm is made with the box centered at the nominal position of the first source in the coordinate list. Coarse centering is performed only if the shifts file is undefined. Care should be taken to choose an appropriate value for this parameter, since it is highly data dependent. Large value should be suspect until the final results are checked to see that the centering did not converge on the wrong coordinates, although the usual result for an inappropriate bigbox size is that the algorithm fails to converge and the task aborts.
- negative = no
- Are the features negative ?
- background = INDEF
- The absolute reference level for the marginal centroid calculation. If background is INDEF, this is set to the mean value (between the thresholds) of the individual sources.
- lower = INDEF
- The lower threshold for the data. Individual pixels less than this value will be given zero weight in the centroids.
- upper = INDEF
- The upper threshold for the data. Individual pixels greater than this value will be given zero weight in the centroids.
- niterate = 3
- The maximum number of centering iterations to perform. The centering will halt when this limit is reached or when the desired tolerance is achieved.
- tolerance = 0
- The tolerance for convergence of the centering algorithm. This is the integral shift of the centering box from one iteration to the next.
- maxshift = INDEFR
- The maximum permitted difference between the predicted shift and the the computed shift for each object. Objects with shifts greater than maxshift are ignored. If maxshift is undefined no shift checking is done.
- verbose = yes
- Print the centers for the individual objects ? If verbose is no only the shifts relative to the reference coordinates will be reported. If no reference image is supplied, verbose is automatically set to yes.
IMCENTROID measures the X and Y coordinates of a list of sources in a list of images and finds the mean X and Y shifts between the input images input and a reference image, where the shifts are defined as the shifts that should be added to the input image coordinates to convert them into the reference coordinates. The task is meant to address the class of two dimensional image registration problems in which the images have the same pixel scale, are shifted relative to each other by simple translations in each axis, and contain enough high signal-to-noise, pointlike sources in common to form good average positions. The basic operation of the task is to find centers for the list of registration objects in the coordinate frame of each image and then to subtract the corresponding centers found in the reference image. The shifts of the objects are averaged for each image.
A list of the X and Y coordinates of the registration objects should be provided in the coordinates file coords . The registration objects do not all have to be common to each frame, rather only that subset of the objects that is contained within the bounds of a given image will be centered. Only the objects that are common to both the given image and the reference will be used to calculate the shifts. The coordinates should be measured in the frame of the reference imagereference . If coarse centering is to be done, which is to say, if no shifts file is provided, then the first registration source should be separated from other sources by at least the maximum expected relative shift.
An initial estimate of the shifts between each of the input images input and the reference image reference is required for the centering algorithm (a marginal centroid) to work. This estimate can be explicitly supplied in the text file shifts where Xshift = Xref -Xin and Yshift = Yref -Y in, or can be generated from the images by measuring the relative shift of the first source listed in the coordinates file coords for each input image. This coarse centering pass requires that the first source be detached from other sources and from the border of each image by a distance that is at least the maximum shift between the reference and input image. This source should be pointlike and have a high signal to noise ratio. The value of the bigbox parameter should be chosen to include the location of the source in each of the images to be aligned while excluding other sources. Large values of bigbox should be held suspect until the final convergence of the centering algorithm is verified, although given a small value for the tolerance , the quality of the final centers is independent of the estimate for the initial shifts. Better convergence may also be obtained by increasing the niterate parameter, although the default value of three should work for most cases. Niterate should be kept small to avoid runaway.
The boxsize parameter controls the size of the centering box for the fine centering pass and should be chosen so as to exclude sky background and other sources while including the wings of the point spread function. The sense of the shifts that are calculated is consistent with the file supplied to the shifts parameter and with that used with the IMSHIFT task.
IMCENTROID may be used with a set of input images which vary in size. This can result in vignetting of the calculated overlap region because of the nature of tasks such as IMSHIFT to preserve the size of an input image. To visualize this, imagine a large reference image and a single small image to be aligned to it, both containing the same registration object which is at the center of each image. IMCENTROID will cause the coordinate system of the small image to be shifted such that the object will be positioned at the same pixel location as in the reference. If the shift is performed, a large fraction of the area of the small image may be shifted outside of its own borders, whereas the physical overlap of the large and small images includes ALL of the pixels of the small image. In the case of such vignetting, IMCENTROID will print a warning message and both the vignetted and unvignetted trim sections. Note that the vignetting will not occur if the small image is used as the reference image.
The vignetting message may also be printed if the input images are all the same size but the reference image is not included in the list. This will occur if the sense of the measured shifts in a coordinate are all positive or all negative since in this case the border of the reference image would have provided one of the limits to the trim section. The reality of this vignetting depends on your point of view.
Note that many of these difficulties are due to the intrinsically fuzzy nature of the process of image registration. This all leads to a few guidelines:
o Include the reference image in the input image list o Use the smallest image as the reference image o Choose the reference image such that the input images are scattered to either side in the shifts in each axis o Align images that are the same size, OR o Pad dissimilar sized images with blanks to the largest size
The algorithm is a "marginal" centroid in which the fit for each axis is performed separately upon a vector created by collapsing the centering box perpendicular to that axis. The centroid is calculated with respect to the level specified by background . If background is INDEF, the reference level for each source in each image is the local mean for those pixels that lie between the lower and upper thresholds. The thresholds are set to the local data minimum or maximum if lower or upper , respectively, are INDEF. If negative is yes, than the marginal vector will be inverted before being passed to the centroid algorithm.
The maximum number of centering iterations and the tolerance for convergence are controlled by niterate and tolerance . Note that the tolerance is an integer value that represents the maximum movement of the centering box between two successive iterations. The default value of 0 requires that the centroid lie within the center pixel of the centering box which is boxsize in extent (note that boxsize must be an odd number). This should normally be the case for bright, circularly symmetric point sources in images with a flat sky background. If the registration sources are not circular symmetric try increasing the tolerance gingerly. If the sky background is not flat, but varies across the image, it can be removed before processing.
1. Calculate the shifts between three images using the first image as a reference image and the list of registration star coordinates in the file "x1.coords".
cl> imcentroid x1,x2,x3 x1 x1.coords
2. Calculate the shifts between a list of images contained in the file "imlist":
pr> imcentroid @imlist x1 x1.coords
3. Perform the centering, but don't calculate the shifts, i.e., don't supply a reference image. Note that the input list of shifts, or a coarse centering pass are still needed:
pr> imcentroid @imlist "" x1.coords
The coarse centering portion of the algorithm can be fooled if the first source on the list is not well separated from other sources, or if the first source has a low signal to noise ratio, or if there is a complicated shape to the background.