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A Semi Supervised Approach to Three Dimensional Human Pose Estimation

Pourdamghani, Nima | 2011

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 42537 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Rabiee, Hamid Reza
  7. Abstract:
  8. In this research, we introduce a semi-supervised manifold regularization framework for hu- man pose estimation. Here we aim the three major challenges in discriminative human pose estimation. We utilize the unlabeled data to reduce the need to labeled data and compen- sate for the complexities in the input space. We model the underlying manifold by a nearest neighbor graph. Due to depth ambiguity which is the main challenge in this problem, the true underlying manifold of the data bends and gets too close to itself is some areas which results in poor graph construction. To solve this problem, we argue that the optimal graph is a subgraph of the k-nearest neighbor graph and employ an estimate of distances in the out- put space to approximate this subgraph. In addition, we use the underlying manifold of the points in the output space to introduce a novel regularization term which captures the cor- relation among the output dimensions. The modified graph and the proposed regularization term are utilized for a smooth regression over both the learned input and output manifolds. Experimental results on various human activities demonstrate the superiority of the proposed algorithm compared to the current state of the art methods
  9. Keywords:
  10. Machine Vision ; Human Pose Estimation ; Gragh Based Method ; Semi-Supervised Algorithm

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