But with the technology Google has licensed from SRI, image stabilization will no longer be a concern ... at least on Android. Google is implementing the SRI tech in its Google Talk application, to deliver better video on Android 3.0+ devices. And that may be only the beginning of Google's computer vision plans.

Image stabilization technology is over 20 years old, with initial applications built for defense use by DARPA. The technology was crucial to autonomous or semi-autonomous vehicles and robotics. Someone driving a tank, for example, would get nauseous in 2 minutes if it weren't for stabilization technologies, Winarsky says.

But these days, the tech has made its way into more benign, consumer-facing efforts, like Google Talk, apparently. Here, the video chatting application captures the video from a device's front-facing camera and compresses the data before transmission. In the compression algorithms, the amount of bandwidth used increases with the amount of motion in a scene.

By stabilizing the video, SRI's software allows the compression to take up fewer bits. Simply put, it's more efficient. It takes less work and fewer resources.

What Could Google Do, Post-Video Stabilization?

Although SRI can't talk for Google or about its future plans in this area, saying only that it "fully hopes to work beyond this app with Google," Winarsky was happy to talk in more general terms about where computer vision technologies are headed.

Once you have the stabilization down, he says, you can then work on things like tracking objects that appear in the frame, tracking the motion of objects and recognizing those objects. Head tracking, for example, was demonstrated at this year's Google I/O where the stabilization technology was used in conjunction with a face-tracking API (application programming interface) that will arrive in a future version of Google's mobile operating system Android.

Put it all together - stabilization, image tracking and image recognition - and you have "augmented reality" (AR), a term that describes technology that lets your device "see" the world in front of its camera lens and then act on that data in some way.

Google, of course, is already experimenting with AR to some extent through its "Google Goggles" application which lets you use pictures to search the Web. Google Goggles can currently identify things like landmarks, books, art, wine and logos, but has recently started recognizing text, too, in order to perform on-the-fly translations between languages.

Facial Recognition in Video?

There are other things that stabilization can help to enable, says Winarsky. For example, facial recognition. Until an image is stabilized, such a thing would not be possible on video. After stabilization though, the same type of algorithms that currently work on still images could be applied to moving video.

Google already uses facial recognition in its online photo-sharing service Picasa (soon to be rebranded as "Google Photos"), so it's not a big leap to assume that Google could introduce something like that to its video applications and services someday. Facial recognition in Google Talk? YouTube? Google Goggles? Who knows?

Case in point: earlier this year, Google denied that it has a facial recognition app in development, after CNN published a report to the contrary, including an on-record statement from a Google employee confirming its existence.  And Google recently rolled out a smart update to search that allows you to search for things using only an image. And guess what? It works for images of people, too.

So why not make people chatting with you on video, seen through your camera lens or those appearing in online videos "Googleable" objects? There's only one reason not to: it's a little creepy. But creepy/awesome is the line Google likes to toe. For the company, it's not a matter of if something is possible - it's only a matter of when is the right time to release it.

Stabilization, on its own, may seem like minor news, but it's an important first step towards a future where the world itself, and all the people in it, are things you can Google just by looking at them.

Initially rolled out for select images only, the capability is now being rolled out for what seems to be the majority of images in Google image search. This is particularly useful when search terms include homonyms, we think; and we applaud Google for removing a major pain point in our searches for pics of Tesla the band as opposed to Tesla the scientist or Tesla the automotive brand.

Note the lilac-colored links under most images. Users can employ this feature for narrowing results to specific people, objects, poses, angles, or even identical images posted on different sites. Here's what happened when we clicked the "similar images" link under a thumbnail of hair metalists Tesla:

And again, when we clicked a thumb that pictured the entire band playing live onstage:

It would be interesting if the similarities became more and more granular, but results appear to occasionally broaden or simply stop refining a few clicks into the search process. For this search, however, we were able to see an entire set of photos from a single concert and multiple sources, which was pretty interesting.

What do our readers think? Are similar images helpful to you in your quest for better image searches?

Now you can annotate images from Wikimedia Commons in a fashion very similar to Flickr. The big difference with the new feature is that annotations can be added by anyone, and no account is necessary. While they don't show up directly in Wikipedia yet, a new version of the system that will appear in the free encyclopedia is under development.

Once you find the image you want, either by clicking through from Wikipedia or searching Wikimedia Commons directly, just click the "add note" button underneath any image on its own page. Then type in your text and either preview or save it. In addition to simple text notes, you can include wiki markup to add links or for styling, and if your annotation fails then you can add it manually through editing the page's code.

The image annotation gadget is basically just some JavaScript written by a Wikimedia volunteer and made standard on the site. If you're a regular user and you don't like the boxes showing up on your images, then you can turn it off via your user preferences. The advantage to logging in to add notes is that a history of all your annotations will show up in your user contributions history. According to the project's FAQ, a version will soon be available for use directly within Wikipedia.

Great for Cities - But not for Sights

While this worked quite well for the examples Google gives in its blog post, a few of our other searches ('Yosemite National Park' for example), did not bring up any photos. Any search for a city, however, always gave us related images from the photo layer in Google Maps. Searches for local restaurants and sites typically don't bring up any images and searches for local sights already brought up search results from Google Images anyway, and from what we can see, nothing has changed there.

It's nice to see, however, that Google now surfaces more images. It is also worth pointing out that yesterday, Google also announced that users can now easily filter Google Images by Creative Commons license, which, until now, was really the domain of Yahoo's Flickr, which hosts the world's largest repository of CC-licensed images.

Competing With Bing

Of course, we also can't help but think that the fact that Google is facing renewed competition from Bing, which also does a nice job of mixing up images with local search results - though not quite as nicely as Google now does. Travel is one of the areas where Bing excels, and it is good to see that the competition in this market is driving all players to innovate and improve their services.