Deep High-order Tensor Convolutional Sparse Coding for Stereo Matching
With the rapid development of science and technology, high-dimensional data emerge one after another. High-order tensors can be used to describe high-dimensional data structure, which can retain the hidden structure of data, but cannot obtain the deep features. Therefore, it is very important to est...
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| Veröffentlicht in: | 2021 3rd International Conference on Robotics and Computer Vision (ICRCV) S. 57 - 62 |
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06.08.2021
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| Abstract | With the rapid development of science and technology, high-dimensional data emerge one after another. High-order tensors can be used to describe high-dimensional data structure, which can retain the hidden structure of data, but cannot obtain the deep features. Therefore, it is very important to establish a deep high order tensor model. In this paper, we propose a deep high-order tensor convolutional sparse coding model, which can automatically learn the deep convolutional kernel. Based on the learned deep convolutional kernel, a two-layer deep dictionary learning model is established. Then, the sparse representation coefficients are respectively solved, and a new weighted matching cost method is constructed, which combines shallow and deep features. The experimental results on the Middlebury 2014 dataset show that the proposed deep high-order tensor convolutional sparse coding is effective for stereo matching. |
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| AbstractList | With the rapid development of science and technology, high-dimensional data emerge one after another. High-order tensors can be used to describe high-dimensional data structure, which can retain the hidden structure of data, but cannot obtain the deep features. Therefore, it is very important to establish a deep high order tensor model. In this paper, we propose a deep high-order tensor convolutional sparse coding model, which can automatically learn the deep convolutional kernel. Based on the learned deep convolutional kernel, a two-layer deep dictionary learning model is established. Then, the sparse representation coefficients are respectively solved, and a new weighted matching cost method is constructed, which combines shallow and deep features. The experimental results on the Middlebury 2014 dataset show that the proposed deep high-order tensor convolutional sparse coding is effective for stereo matching. |
| Author | Cui, Wenjing Cheng, Chunbo |
| Author_xml | – sequence: 1 givenname: Wenjing surname: Cui fullname: Cui, Wenjing email: wjcui@hbpu.edu.cn organization: Hubei Polytechnic University,School of Mathematics and Physics,Huangshi,China – sequence: 2 givenname: Chunbo surname: Cheng fullname: Cheng, Chunbo email: bccheng@hbpu.edu.cn organization: Huazhong University of Science and Technology,School of Mathematics and Statistics,Wuhan,China |
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| Snippet | With the rapid development of science and technology, high-dimensional data emerge one after another. High-order tensors can be used to describe... |
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| SubjectTerms | Computer vision Convolutional codes convolutional sparse coding Costs Data structures Deep learning dictionary learning Encoding high-order tensor stereo matching Tensors |
| Title | Deep High-order Tensor Convolutional Sparse Coding for Stereo Matching |
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