"Imaging" is a broad term meaning the ability to transform, interpret and/or associate an image. Sounds pretty easy, right? But when you consider what is actually involved, it's pretty hard. Most modern digital cameras (and other photo-capture devices) are packed with a host of "automatic" imaging, such as noise removal, contrast/exposure enhancement, etc., up to red-eye removal and photo album aggregation. All of which implies there are (hopefully reliable!) algorithms available to improve the quality of an image (and thus improve its value).

However, the way you will use an image (called its "workflow") impacts what algorithms you will use to clean it up, improve its quality, and otherwise transform it. Normal metrics for "image quality", for example, are not as important in security printing and imaging as are the more arcane concepts of inspectability, authenticability and forensics-capability. Consider, as an exemplar, if I have added information--such as a 2D bar code or a digital watermark [hidden, or "steganographic" information]--to an image, then whatever I do with my imaging should be focused on helping me reliably extract that information rather than improving the aesthetics of the image.

How hard can that be, you ask? Let's look at four photos that each contain one set of identical information; namely, the GPS location that the image was taken from.

Picture 1 is the new Sao Paulo bridge by day, from the 31st floor:

Picture 2 is the same bridge, from the same floor, with a different aspect ratio:

The third is taken from the same GPS location, 30 floors lower, and at dark:

And the last image is taken from the same GPS location, 6 stories up, in the opposite direction:

What information do these pictures have in common aside from the GPS location? Picture 1 and 2 are pretty similar to the human viewer, but to the imaging algorithm have a number of distinctions. Perspective, aspect ratio, contrast and exposure all differ considerably. Most image-clustering technologies, however, can aggregate (find similar) these two. But if the bridge were watermarked, would the photos equally represent those watermarks? Would the same bridge in Picture 3 also aggregate with Pictures 1 and 2?  Humans would say yes, but machine algorithms are not so sure. Picture 4, not a chance for the machine (or for any human who did not have the memory of both images), without the GPS information.

This represents (somewhat figuratively) some of the challenge involved in security imaging. In practice, we are not usually required to associate Picture #4 with Pictures 1-3 except through metadata (image header) search. In future blogs, I will discuss how the imaging is actually able to work (and work well!) on Pictures 1-3.

-Steve