Diverse M-best solutions in markov random fields
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Practicality of accelerometer side channels on smartphones
Proceedings of the 28th Annual Computer Security Applications Conference
Appearance sharing for collective human pose estimation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
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We describe a method for generating N-best configurations from part-based models, ensuring that they do not overlap according to some user-provided definition of overlap. We extend previous N-best algorithms from the speech community to incorporate non-maximal suppression cues, such that pixel-shifted copies of a single configuration are not returned. We use approximate algorithms that perform nearly identical to their exact counterparts, but are orders of magnitude faster. Our approach outperforms standard methods for generating multiple object configurations in an image. We use our method to generate multiple pose hypotheses for the problem of human pose estimation from video sequences. We present quantitative results that demonstrate that our framework significantly improves the accuracy of a state-of-the-art pose estimation algorithm.