I developed an informal way of marking the locations of ground control points (GCPs) in Agisoft Photoscan that seems to work in cases where the targets are blurry or lack a well-defined center. It seems to have improved the accuracy of the markers, so I thought I'd describe it here in case others find it useful. This method has the advantages of being able to work with image data sets that are blurry or don't have a clearly marked center pattern (e.g., an absence of intersection of orthogonal lines, coded targets, or circle with a center), or lack sufficient resolution to clearly identify a pixel representing the center. It seems to work well even in oblique images where the target has low contrast or is viewed from a low angle. It could also be applied to markers for other features in images used for 3D digital reconstructions using photogrammetry.
Basically, the method involves looking for the centroid or weighted average of the pixels representing the target GCP. I look for patterns of symmetry in the pixels representing the target, while paying attention to both color and intensity. I liken this to the highlight detection algorithm in the RTIBuilder software, which finds the center of highlights on the reflective spheres. I suppose that a similar algorithm could be written to detect arbitrary GCPs in Photoscan, which wouldn't require coded targets. It requires only that the targets have a symmetric shape and sufficient contrast to be identified visually against the surrounding terrain. In many cases, the presence of a red and green fringe (chromatic aberration) around the edges of high-contrast targets helped to identify symmetries that could help locate the center of the targets.
As an example, this method allowed a series of GCP targets to be marked so the average projection error was limited to less than 0.25 pixels. When the average projection error for a target was much higher than this value, the marker positions could easily be reviewed and the markers on images that contributed to the higher errors were removed or adjusted. I found that it significantly improved the calculated error values. Although the method as described here is subjective and could be said to introduce bias, I found that it helped me identify errors in my placement of the markers and correct them. In this case, the targets "constructed of white sheets were deployed... and their positions recorded by dGPS to an estimated precision of 0.1 m." (James, M. R. and Robson, S.  Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application, J. Geophysical Res., 117, F03017, doi:10.1029/2011JF002289, accessed 14 November 2015).