Today we’re announcing that we just raised 7M in Series A led by Foundry Group, representing the first major investment in Occipital.

Over the last year, what we’ve launched publicly is 360 Panorama – a popular app which lets you capture panoramas in seconds and share them as interactive 360 views. But what you might not know is that 360 Panorama is just the tip of the iceberg.

Your smartphone’s computational reach into its surroundings ends at its touchscreen surface. To your device, the real world isn’t a canvas of interactivity. Instead, it’s little more than a grid of pixels that might as well be random. We’re changing that. We’re using computer vision to make real world environments computationally interactive and fun, thereby extending the computational reach of your device into the visual space around you.

This concept is bigger than Occipital can handle alone, so we’re launching a platform that other developers can leverage. We’ll take care of the computer vision, allowing developers to focus on creating new experiences.

We’re also announcing new additions to our board of directors – Jason Mendelson and Brad Feld of Foundry Group, Manu Kumar of K9 Ventures and Gary Bradski of Willow Garage.

We’ve known Jason and Brad since 2008 when we joined TechStars. We’ve experienced first-hand their open and engaged approach to helping entrepreneurs. Jason, Brad, and the whole Foundry team, are awesome, and as part of their HCI theme, they share our belief that computer vision will fundamentally change the way we interact with our surroundings.

Dr. Manu Kumar is a successful entrepreneur, founder of K9 Ventures, and has a PhD from Stanford in eye-tracking HCI. We can’t overstate how helpful he has been since we met him three years ago. It’s about time we figured out how to work together officially.

Dr. Gary Bradski is the creator of OpenCV – a computer vision library used by thousands of computer vision researchers and engineers around the world. These days he’s Senior Scientist at Willow Garage where he works on advancing the state of robot vision. Gary agrees that we’re on the cusp of something huge in mobile computer vision and he significantly expands the technical gravity of our board of directors.

Welcome, everyone, to the Occipital team.

It’s going to be a wild ride – and where we’re going, we don’t need roads.

In the 70′s, we thought it would be easy to create machines that could see.  We were wrong.  But today, we’re on the cusp of something exciting.

If you can define the vision problem precisely, odds are, we can build a machine that rivals or exceeds human ability: We can build machines which are better at recognizing faces than people.  We’re wired to recognize a few hundred or a few thousand faces, but security software can scan for one in a million.  It’s not just for security, anymore.  We can do this across the web, and recently, in our own photo sets.

In swimming pools, Lifeguards aren’t always vigilant, but increasingly, computer vision systems are.

We’re getting better at taking large collections of photographs and recreating full 3D (or 4D) scenes.  Photo tourism is already changing the way we review large collections of photos in popular areas.

We still suck at building vision software that can perform general object recognition as well as humans.  But some groups are working on that.  I don’t think it will take long before these systems rival human ability for any visual task that you can perform in under a second.

The most exciting thing is that the game doesn’t stop when we match human ability across a broad spectrum of tasks.  Instead, it gets more interesting.  Today, we can’t see through walls, and we can’t recognize everyone in a crowd.  We can’t jump three-hundred feet in the air to get a birds-eye view.  We can’t recognize every species of plant and animal.  We can’t read text in more than a handful of languages.  We can’t see beyond the human visual spectrum.  You get the idea.  It’s the dawn of an exciting time.

You’ve been there.  We all have.  We’ve all tried to make the round peg fit into the square hole.  Only this time I’m not referring to it figuratively.  I mean we’ve all literally tried to make the round peg fit.  It didn’t fit, so we tried to jam it.  Occasionally it actually works out:  “Who needs the square peg anyway?  Square pegs are a total ripoff.”  But sometimes, you give up and shell out for the square peg.  And it’s incredible how well it gets the job done.  You never seem to fully appreciate the power of having the right tool until you’ve been in one of these situations.  You also never seem to learn your lesson, “Hmm… I wonder if this square thing can do an octagonal hole…”

You’re probably wondering, what does this have to do with anything?

I’m actually writing about a new tool that was recently built by Nicolas Pinto and several other artificial vision researchers at MIT.  The goal: learn to replicate the human vision system.  One of the big challenges in artificial vision research is testing your hypotheses efficiently.  Since we’re trying to replicate the human vision system, we can look to biological neuroscience for qualitative hints on how our artificial systems should work.  But there are still so many unknowns that, even when we’ve nailed down an algorithm, we end up with a wide swath of magical parameters to fill in.  Artificial vision is so computationally complex, that running even a small-scale test with one parameter setting could be time-consuming on a normal CPU.  If the test “fails,” should we blame the parameter setting, the scale, or the whole approach?

Nicolas and the team recently assembled a computer with sixteen graphics processors.  The idea is that this new tool can run tests so much faster (kinda like Devver!), that they can afford to toggle more parameters and run at a larger scale.  I’ve personally been frustrated with the inability to run a lot of tests, so I envy this new square peg, and intend to follow what they manage to learn with it.

In case you’re wondering what this thing looks like:

(Thanks Nicolas for permission to republish the photo.)