Colorado Springs and Kinect

I have again neglected my blog for sometime due to deadlines and other aside work. I just realized I never got myself some time to write about my visit to Colorado Springs for CVPR 2011 during the summer. Actually there is not much for me to say about Colorado Springs since I hardly visited any places beyond the hotel so I will just mainly refer about one particular paper presented in this conference.

CVPR 2011 might perhaps be remembered as the conference where the research paper Real-time Human Pose Recognition in Parts from Single Depth Images (The Kinect paper!) by Microsoft Research was presented. (Ok, this is obviously an overstatement, quality of research at this conference is really high) Microsoft Kinect is a product that has had a big impact that goes beyond gaming. This is a very iconic example of Computer Vision that works and is readily available to the world.

Wearing a cap and being Kinect captured

Kinect has inspired hackers (http://kinecthacks.net/), artists (http://artandcode.com/3d/) and general technology enthusiasts since its introduction some time ago. It has also inspired researchers to create new algorithms that can clean the data captured by the Kinect sensors and make the most out of it or just play with it (here http://acberg.com/kinect/ some kinect hacking in Matlab by Alex Berg, one of my vision professors in Stony Brook). The picture I included in this blog post was ironically captured in our fancy Motion Capture Lab using the inexpensive capturing device from Microsoft. People here have also been working on Kinect with applications to Music performances and Motion identification. (More links to be added later...). Update: Interactive Music using Kinect: http://tamaraberg.com/papers/kinect_music.pdf

Although I didn't stay for the whole week of the conference I also presented a paper in CVPR 2011 about automatically estimating photo-quality and user engagement for photographs titled: High Level Describable Attributes for Predicting Aesthetics and Interestingness. Our goal was to use Computer Vision to recognize what are the kind of photographs that users think are cool without explicitly having to ask them what is cool? (Note: Users might not even realize what are the individual things that make them judge something as cool or interesting).