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Street Slide - StreetView Microsoft Style

     
10:17 am on Jul 31, 2010 (gmt 0)

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Although it's just at the demo stage Street Slide is worth a look:

[research.microsoft.com...]

I've often found StreetView a little cumbersome when trying to locate a particular building - this looks easier so I hope it sees the light of day.
3:58 pm on July 31, 2010 (gmt 0)

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Some very nice ideas. I'd welcome anything that would make navigation in a street view less cumbersome and awkward.

And if they could actually get the underlying data correct and consistent that would be a bit of a bonus.
11:22 pm on Aug 1, 2010 (gmt 0)

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I'd love to see this implemented. It's an elegant approach, and computationally less demanding than other image stitching approaches that I've seen presented, which involve the meshing of a huge number of individual images.

The most impressive of those presentations, not coincidentally, were made by someone also connected with Microsoft... Blaise AgŁera y Arcas, "the architect of Bing Maps at Microsoft", on two TED Talk videos. The concepts behind Photosynth video, in particular, I feel, have profound implications for all of search, not simply local imagery, and suggest why personalization and user data are of such interest to the various search engines. Note the use of the phrase "augmented reality" in the second video.

Blaise Aguera y Arcas demos Photosynth
http://www.ted.com/talks/blaise_aguera_y_arcas_demos_photosynth.html
[ted.com...]

Blaise Aguera y Arcas demos augmented-reality maps
http://www.ted.com/talks/blaise_aguera.html
[ted.com...]

Google has been working with Google Goggles and Google Similar Images... in their current forms not nearly so impressive as what I've seen from Microsoft. I made some comments about these in the Google forum that, together with the above, hint how local image search and street views, together with GPS data from a camera or phone, might ultimately tie in....

Google Image Recognition
http://www.webmasterworld.com/google/4153195.htm
[webmasterworld.com...]

Explanations of image recognition software (and image stitching software) that I've read suggest that an image can be broken down into polygons, and then matched with other images using "corners" of shapes defined by the software.

It works ok for specific things, major landmarks, anything with a barcode, etc...

Clearly, you need a starting point for matching. You'd otherwise have to crunch an impossible amount of data to find a match. The more data you have, also, the more refined and accurate the entire model becomes.
 

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