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Displaying 1-4 out of 4 total
Training Deformable Part Models with Decorrelated Features
Found in: 2013 IEEE International Conference on Computer Vision (ICCV)
By Ross Girshick,Jitendra Malik
Issue Date:December 2013
pp. 3016-3023
In this paper, we show how to train a deformable part model (DPM) fast-typically in less than 20 minutes, or four times faster than the current fastest method-while maintaining high average precision on the PASCAL VOC datasets. At the core of our approach ...
Efficient Human Pose Estimation from Single Depth Images
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Jamie Shotton,Ross Girshick,Andrew Fitzgibbon,Toby Sharp,Mat Cook,Mark Finocchio,Richard Moore,Pushmeet Kohli,Antonio Criminisi,Alex Kipman,Andrew Blake
Issue Date:December 2013
pp. 2821-2840
We describe two new approaches to human pose estimation. Both can quickly and accurately predict the 3D positions of body joints from a single depth image without using any temporal information. The key to both approaches is the use of a large, realistic, ...
Efficient regression of general-activity human poses from depth images
Found in: Computer Vision, IEEE International Conference on
By Ross Girshick,Jamie Shotton,Pushmeet Kohli,Antonio Criminisi,Andrew Fitzgibbon
Issue Date:November 2011
pp. 415-422
We present a new approach to general-activity human pose estimation from depth images, building on Hough forests. We extend existing techniques in several ways: real time prediction of multiple 3D joints, explicit learning of voting weights, vote compressi...
Visual object detection with deformable part models
Found in: Communications of the ACM
By David McAllester, Deva Ramanan, Pedro Felzenszwalb, Ross Girshick
Issue Date:September 2013
pp. 97-105
We describe a state-of-the-art system for finding objects in cluttered images. Our system is based on deformable models that represent objects using local part templates and geometric constraints on the locations of parts. We reduce object detection to cla...