Journal ArticleDOI
Multimodal Deep Autoencoder for Human Pose Recovery
TLDR
A novel pose recovery method using non-linear mapping with multi-layered deep neural network and back-propagation deep learning to obtain a unified feature description by standard eigen-decomposition of the hypergraph Laplacian matrix.Abstract:
Video-based human pose recovery is usually conducted by retrieving relevant poses using image features. In the retrieving process, the mapping between 2D images and 3D poses is assumed to be linear in most of the traditional methods. However, their relationships are inherently non-linear, which limits recovery performance of these methods. In this paper, we propose a novel pose recovery method using non-linear mapping with multi-layered deep neural network. It is based on feature extraction with multimodal fusion and back-propagation deep learning. In multimodal fusion, we construct hypergraph Laplacian with low-rank representation. In this way, we obtain a unified feature description by standard eigen-decomposition of the hypergraph Laplacian matrix. In back-propagation deep learning, we learn a non-linear mapping from 2D images to 3D poses with parameter fine-tuning. The experimental results on three data sets show that the recovery error has been reduced by 20%–25%, which demonstrates the effectiveness of the proposed method.read more
Citations
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Journal ArticleDOI
A survey of deep neural network architectures and their applications
TL;DR: This work was supported in part by the Royal Society of the UK, the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany.
Journal ArticleDOI
A review on neural networks with random weights
TL;DR: This paper objectively reviews the advantages and disadvantages of N NRW model, tries to reveal the essence of NNRW, and provides some useful guidelines for users to choose a mechanism to train a feed-forward neural network.
Journal ArticleDOI
1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data
Osama Abdeljaber,Onur Avci,Mustafa Serkan Kiranyaz,Boualem Boashash,Boualem Boashash,Henry A. Sodano,Daniel J. Inman +6 more
TL;DR: This paper presents an enhanced CNN-based approach that requires only two measurement sets regardless of the size of the structure and successfully estimated the actual amount of damage for the nine damage scenarios of the benchmark study.
Journal ArticleDOI
3D Human pose estimation
TL;DR: An extensive experimental evaluation of state-of-the-art approaches in a synthetic dataset created specifically for 3D human pose estimation, which along with its ground truth is made publicly available for research purposes.
Journal ArticleDOI
Multi-view low-rank sparse subspace clustering
Maria Brbic,Ivica Kopriva +1 more
TL;DR: An approach to multi-view subspace clustering that learns a joint subspace representation by constructing affinity matrix shared among all views is presented, relying on the importance of both low-rank and sparsity constraints in the construction of the affinity matrix.
References
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