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Submitted by Emmanuel Boidot on Thu, 12/27/2012 - 12:18

Exploiting local and global patch rarities for saliency detection

Borji, A., and L. Itti, "Exploiting local and global patch rarities for saliency detection", CVPR, 2012.
PDF icon cvpr2012c.Borji_.Itti_.printed.pdf
Submitted by Emmanuel Boidot on Thu, 12/27/2012 - 12:16

Learning sparse covariance patterns for natural scenes

Wang, L., Y. Li, J. Jia, J. Sun, D. Wipf, and J. J. M. Rehg, "Learning sparse covariance patterns for natural scenes", CVPR, 2012.
PDF icon cvpr2012.Wang_.Li_.Rehg_.etal_.printed.pdf
Submitted by Emmanuel Boidot on Thu, 12/27/2012 - 11:58

Classifying Large-Scale Environment Shapes with Linear Optical Flow Templates

Roberts, R., N. Srinivasan, and F. Dellaert, "Classifying Large-Scale Environment Shapes with Linear Optical Flow Templates", CVPR, 2012.
PDF icon cvpr2012.Roberts.Srinivasan.Dellaert.submitted.pdf
Submitted by Emmanuel Boidot on Thu, 12/27/2012 - 11:44

Social interactions: A first-person perspective

Fathi, A., J. K. Hodgins, and J. J. M. Rehg, "Social interactions: A first-person perspective", CVPR, 2012.
PDF icon cvpr2012.Fathi_.Hodgins.Rehg_.printed.pdf
Submitted by Emmanuel Boidot on Thu, 12/27/2012 - 11:40

Learning to recognize objects in egocentric activities

Fathi, A., X. Ren, and J. J. M. Rehg, "Learning to recognize objects in egocentric activities", CVPR, 2011.
PDF icon cvpr2011.Fathi_.Ren_.Rehg_.printed.pdf
Submitted by Emmanuel Boidot on Thu, 12/27/2012 - 11:35

Threat-based hazard avoidance for semi- autonomous vehicles using nonlinear model predictive control

Peters, S. S. C., S.. J. Anderson, T. Pilutti, E. H. Tseng, and K. Iagnemma, "Threat-based hazard avoidance for semi- autonomous vehicles using nonlinear model predictive control", IEEE Transactions on Control Systems Technology, 2012.
PDF icon cst12.Peters.Anderson.Pilutti.etal_.submitted.pdf

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Dynamics and Control Systems Lab

at Georgia Institute of Technology

 

The Borg Lab

at Georgia Institute of Technology

 

Robotic Mobility Group

at Massachusetts Institute of Technology

 

Aerospace Robotics and Embedded Systems Laboratory

at Massachusetts Institute of Technology

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About us

We are three research groups from Georgia Tech, the Massachusetts Institute of Technology, and the University of Southern California, collaborating to perform basic research on high-speed autonomous driving.  We are most interested in researching biologically-inspired methods in the realms of both perception and control.

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Acknowledgment

This work was supported by the Army Research Office under MURI Award W911NF-11-1-0046.