Learning multi-view manifold for single image based modeling

Cui, Jiahao; Ли, Шуай; Xia, Qing; Hao, Aimin; Qin, Hong

doi:10.1016/j.cag.2019.05.030

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Symmetry 2020, 12, 434 3 of 13 [43]. The Wasserstein-driven low-dimensional manifold model (W-LDMM) can be used for noise estimation, image denoising and noisy image inpainting tasks [44].…”

Section: Methodsmentioning

confidence: 99%

“…The weights of the iterative manifold embedding (IME) layer are learned by unsupervised strategy, which has been used to analyze the intrinsic manifolds of data sets with missing data [42]. The distribution of image data in multi-view manifold space can be captured by Multi-view Generative Adversarial Network (GAN), which can map the shape and view manifolds in a lower dimensionality latent space [43]. The Wasserstein-driven low-dimensional manifold model (W-LDMM) can be used for noise estimation, image denoising and noisy image inpainting tasks [44].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Zhu

Lou

et al. 2020

Symmetry

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Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Zhu

Lou

et al. 2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…The generative models apply deep generative models that apply adversarial training to obtain information from multiple views [14]. These methods try to extract correlations between the views through GANs which has shown promising results on 3D skeletal data [15]. These methods are largely focused on 3D models, where a single image 3D model is applied to GANs to generate a multiple views for recognition using deep networks.…”

Section: Related Backgroundmentioning

confidence: 99%

Learning multiview deep features from skeletal sign language videos for recognition

Shaik¹,

Mareedu²,

Kishore³

2021

Turk J Elec Eng & Comp Sci

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The most challenging objective in machine translation of sign language has been the machine's inability to learn inter occluding finger movements during an action process. This work addresses the problem of teaching a deep learning model to recognize differently oriented skeletal data. The multi view 2D skeletal sign language video data is obtained using 3D motion captured system. Total 9 views of the signer were used for training the proposed network and the remaining 6 for testing and validation. In order to obtain multi view deep features for recognition, we proposed an end-to-end trainable multi stream convolutional neural network (CNN) with late feature fusion. The fused multi view features are then inputted to a two-layer dense and a decision making SoftMax. The proposed CNN employs numerous layers to characterize view correspondence to generate maximally discriminative features. This study is important to understand the effects of multi view data processing by CNNs for sign language recognition in decoding joint spatial information. Further, deeper perspectives were developed into multi view processing of CNNs by applying skeletal action data.

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Skip-StyleGAN: Skip-Connected Generative Adversarial Networks for Generating 3D Rendered Image of Hand Bone Complex

Ahn

Lee

Choi

2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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Learning multi-view manifold for single image based modeling

Cited by 4 publications

References 27 publications

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Learning multiview deep features from skeletal sign language videos for recognition

Skip-StyleGAN: Skip-Connected Generative Adversarial Networks for Generating 3D Rendered Image of Hand Bone Complex

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