Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

@article{Tang2014LatentRF,
  title={Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture},
  author={Danhang Tang and Hyung Jin Chang and Alykhan Tejani and Tae-Kyun Kim},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2014},
  pages={3786-3793}
}
In this paper we present the Latent Regression Forest (LRF), a novel framework for real-time, 3D hand pose estimation from a single depth image. In contrast to prior forest-based methods, which take dense pixels as input, classify them independently and then estimate joint positions afterwards, our method can be considered as a structured coarse-to-fine search, starting from the centre of mass of a point cloud until locating all the skeletal joints. The searching process is guided by a learnt… CONTINUE READING
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